A wide selection of undergraduate and graduate courses is offered at the University of Toronto for students interested in analytics and artificial intelligence (A/AI) engineering. Below is a selection of these courses, linked to corresponding course descriptions available on U of T webpages. When considering these courses, students should pay attention to course requirements of their intended degree, emphasis and/or certificate, as well as any pre-requisites, exclusions or restrictions to enrolling into individual courses.
General A/AI Courses
| Code | Course Name | Campus | Department | School | Description |
|---|---|---|---|---|---|
| FSC340H5 | Research Design | Mississauga | Anthropology | University of Toronto Mississauga | This course introduces students to common methods of research design and the nature of data collection. Students will learn how to pose a meaningful research questions to select appropriate data types to define variables examine bias confounding factors and select appropriate statistics that address their purpose. [24L 12S] |
| HSC301H5 | Data and Information Visualization | Mississauga | Biology | University of Toronto Mississauga | This course presents the principles of information design: the clear concise and truthful presentation of data in static and interactive graphics. Visualization is used to explore data reveal patterns and to communicate to different audiences. Topics range from human visual perception and cognition to the critical interpretation of design and accuracy in information graphics. Practical application of course material will require students to develop information graphics for peer review and critique. [24L 12P] |
| CCT226H5 | Data Analysis I (DEM) | Mississauga | Institute of Communication and Culture | University of Toronto Mississauga | This course introduces students to the basic tools of data analysis most particularly statistics and modeling that are critical for subsequent courses in Marketing and Data Analysis II. Students are introduced to basic principles of descriptive and inferential statistics with a focus on the types of data that they will typically encounter in a digital environment. [24L 12P] |
| CSC311H5 | Introduction to Machine Learning | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods decision trees linear models and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy. Basics of reinforcement learning.[24L 12T] |
| CSC384H5 | Introduction to Artificial Intelligence | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | A broad introduction to the sub-disciplines of AI. Core topics: search methods game playing and rule-based systems. Overview of: natural language understanding knowledge representation reasoning planning vision robotics learning and neural networks. Assignments provide practical experience both theory and programming of the core topics. [24L 12T] |
| CSC420H5 | Introduction to Image Understanding | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | This class is an introduction to fundamental concepts in image understanding the sub-discipline of artificial intelligence that tries to make the computers "see". It will survey a variety of interesting vision problems and techniques. Specifically the course will cover image formation features object and scene recognition and learning multi-view geometry and video processing. It will also feature recognition with RGB-D data. The goal of the class will be to grasp a number of computer vision problems and understand basic approaches to tackle them for real-world applications. [24L 12T] |
| STA215H5 | Introduction to Applied Statistics | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | This course introduces the basic concepts logic and issues that form statistical reasoning. Topics include descriptive statistics exploratory data analysis elementary probability sampling distributions point and interval estimation hypothesis testing for normal and binomial data and regression analysis. [36L 12T] |
| STA220H5 | The Practice of Statistics I | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | An introductory course in statistical concepts and methods emphasizing exploratory data analysis for univariate and bivariate data sampling and experimental designs basis probability models estimation and tests of hypothesis in one-sample and comparative two-sample studies. A statistical computing package is used but no prior computing experience is assumed. [24L 12T] |
| STA221H5 | The Practice of Statistics II | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | A sequel to STA220H5 emphasizing major methods of data analysis such as analysis of variance for one factor and multiple factor designs regression models categorical and non-parametric methods. [24L 12T] |
| STA258H5 | Statistics with Applied Probability | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | A survey of statistical methodology with emphasis on the relationship between data analysis and probability theory. Topics covered include descriptive statistics limit theorems sampling distribution point and interval estimation both classical and bootstrap hypothesis testing both classical and bootstrap permutation tests contingency tables and count data. A statistical computer package will be used. [36L 12T] |
| STA304H5 | Surveys Sampling and Observational Data | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | The sample survey is a widely used technique for obtaining information about a large population at relatively small cost. Only probability samples can provide both an estimator and a measure of sampling error from the data itself. In addition to sampling error non-sampling errors (refusals not-at-home lies inaccuracies etc.) are always present and can produce serious biases. The course covers: design of surveys sources of bias randomized response surveys. Techniques of sampling; stratification clustering unequal probability selection. Sampling inference estimates of population mean and variances ratio estimation observational data; correlation vs. causation missing data sources of bias. [36L 12T] |
| STA314H5 | Introduction to Statistical Learning | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | A thorough introduction to the basic ideas in supervised statistical learning with a focus on regression and a brief introduction to classification. Methods covered will include multiple linear regression and its extensions k-nn regression variable selection and regularization via AICBIC Ridge and lasso penalties non-parametric methods including basis expansions local regression and splines generalized additive models tree-based methods bagging boosting and random forests. Content will be discussed from a statistical angle putting emphasis on uncertainty quantification and the impact of randomness in the data on the outcome of any learning procedure. A detailed discussion of the main statistical ideas behind crossvalidation sample splitting and re-sampling methods will be given. Throughout the course R will be used as software a brief introduction will be given in the beginning. [36L 12T] |
| STA315H5 | Advanced Statistical Learning | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | The second part of the course will focus on basic ideas in classification problems including discriminant analysis and support vector machine and unsupervised learning techniques such as clustering principal component analysis independent component analysis and multidimensional scaling. The course will also cover the modern statistics in the "big data" area. The high dimensional problems when p >> n and n >> p will be introduced. In addition the students will be formed as groups to do data analysis projects on statistical machine learning and present their findings in class. This will prepare them for future careers in industry or academia. [36L12T] |
| STA437H5 | Applied Multivariate Statistics | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical correlations; multivariate analysis of variance; profile analysis and curve fitting for repeated measurements; classification and the linear discriminant function. There will be extensive use of statistical computing packages. [36L 12T] |
| STA441H5 | Methods of Applied Statistics | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | Vocabulary of data analysis Tests of statistical significance Principles of research design Introduction to unix and SAS Applications of statistical methods such as Multiple regression Factorial ANOVA Mixed linear models Multivariate analysis of variance Repeated measures Logistic regression Generalized linear models Permutation tests and Bootstrapping. [36L 12T] |
| SOC350H5 | Quantitative Analysis | Mississauga | Sociology | University of Toronto Mississauga | The course is a continuation of SOC222H5 (Measuring the Social World) ) and introduces students to more advanced applications of regression analysis. In addition to producing and interpreting regression models this course also focuses on diagnostic tools for addressing outliers and multicolinearity as well as regression with categorical independent variables and dependent variables (including a basic introduction to logistic regression). This course is mainly project based. Students will develop their own research questions and hypotheses and use statistical software to analyze data in order to provide evidence for their hypotheses. All students in the Sociology and Criminology Law and Society Specialist programs are required to take this course. [24L 11P] |
| JOUB20H3 | Interactive: Data and Analytics | Scarborough | Dept. of Arts Culture & Media (UTSC) | University of Toronto Scarborough | Building the blending of traditional skills in reporting and writing with interactive production protocols for digital news. The course provides an introduction to web development and coding concepts. This course is taught at Centennial College and is open only to students in the Specialist (Joint) program in Journalism. |
| CSCC11H3 | Introduction to Machine Learning and Data Mining | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods decision trees linear and non-linear models class-conditional models neural networks and Bayesian methods. Clustering algorithms and dimensionality reduction. Model selection. Problems of over-fitting and assessing accuracy. Problems with handling large databases. |
| CSCD84H3 | Artificial Intelligence | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | A study of the theories and algorithms of Artificial Intelligence. Topics include a subset of: search game playing logical representations and reasoning planning natural language processing reasoning and decision making with uncertainty computational perception robotics and applications of Artificial Intelligence. Assignments provide practical experience of the core topics. |
| STAA57H3 | Introduction to Data Science | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Reasoning using data is an integral part of our increasingly data-driven world. This course introduces students to statistical thinking and equips them with practical tools for analyzing data. The course covers the basics of data management and visualization sampling statistical inference and prediction using a computational approach and real data. |
| STAC33H3 | Introduction to Applied Statistics | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | This course introduces students to statistical software such as R and SAS and its use in analyzing data. Emphasis will be placed on communication and explanation of findings. Students will be required to write a statistical report. |
| STAC50H3 | Data Collection | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | The principles of proper collection of data for statistical analysis and techniques to adjust statistical analyses when these principles cannot be implemented. Topics include: relationships among variables causal relationships confounding random sampling experimental designs observational studies experiments causal inference meta-analysis. Statistical analyses using SAS or R. |
| STAC51H3 | Categorical Data Analysis | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Statistical models for categorical data. Contingency tables generalized linear models logistic regression multinomial responses logit models for nominal responses log-linear models for two-way tables three-way tables and higher dimensions models for matched pairs repeated categorical response data correlated and clustered responses. Statistical analyses using SAS or R. |
| STAC53H3 | Applied Data Collection | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | This course introduces the principles objectives and methodologies of data collection. The course focuses on understanding the rationale for the various approaches to collecting data and choosing appropriate statistical techniques for data analysis. Topics covered include elements of sampling problems simple random sampling stratified sampling ratio regression and difference estimation systematic sampling cluster sampling elements of designed experiments completely randomized design randomized block design and factorial experiments. The R statistical software package is used to illustrate statistical examples in the course. Emphasis is placed on the effective communication of statistical results. |
| STAC58H3 | Statistical Inference | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Principles of statistical reasoning and theories of statistical analysis. Topics include: statistical models likelihood theory repeated sampling theories of inference prior elicitation Bayesian theories of inference decision theory asymptotic theory model checking and checking for prior-data conflict. Advantages and disadvantages of the different theories. |
| STAC67H3 | Regression Analysis | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Orthogonal projections. Univariate normal distribution theory. The linear model and its statistical analysis residual analysis influence analysis collinearity analysis model selection procedures. Analysis of designs. Random effects. Models for categorical data. Nonlinear models. Instruction in the use of SAS. |
| STAD57H3 | Time Series Analysis | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | An overview of methods and problems in the analysis of time series data. Topics covered include descriptive methods filtering and smoothing time series identification and estimation of times series models forecasting seasonal adjustment spectral estimation and GARCH models for volatility. |
| STAD68H3 | Advanced Machine Learning and Data Mining | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Statistical aspects of supervised learning: regression regularization methods parametric and nonparametric classification methods including Gaussian processes for regression and support vector machines for classification model averaging model selection and mixture models for unsupervised learning. Some advanced methods will include Bayesian networks and graphical models. |
| STAD80H3 | Analysis of Big Data | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Big data is transforming our world revolutionizing operations and analytics everywhere from financial engineering to biomedical sciences. Big data sets include data with high-dimensional features and massive sample size. This course introduces the statistical principles and computational tools for analyzing big data: the process of acquiring and processing large datasets to find hidden patterns and gain better understanding and prediction and of communicating the obtained results for maximal impact. Topics include optimization algorithms inferential analysis predictive analysis and exploratory analysis. |
| GGRC42H3 | Making Sense of Data: Applied Multivariate Analysis | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | This course introduces students to the main methods of multivariate analysis in the social sciences with an emphasis on applications incorporating spatial thinking and geographic data. Students will learn how to evaluate data quality construct analysis datasets and perform and interpret multivariate analyses using the R statistical programming language. |
| MGEC11H3 | Introduction to Regression Analysis | Scarborough | Management (UTSC) | University of Toronto Scarborough | This course will develop the knowledge and skills necessary to obtain and analyze economic data providing an introduction to the use and interpretation of regression analysis. Students will learn how to estimate regressions undertake hypothesis tests and critically assess statistical results. Students will be required to write a major analytical report. Enrolment is limited to students registered in programs requiring this course. |
| CHE223H1 | Statistics | St. George | Chemical Engineering and Applied Chemistry | Faculty of Applied Science & Engineering | Analysis of data using statistics and design of experiments. Topics include probability properties of the normal distribution confidence intervals hypothesis testing fitting equations to data analysis of variance and design of experiments. The tutorial involves in part the application of commercial software to interpret experimental data as obtained in Chemical Engineering laboratories. |
| * Course offering pending approval by Faculty Council for 2018-19 academic year." | |||||
| CHE507H1 | Data-based Modelling for Prediction and Control | St. George | Chemical Engineering and Applied Chemistry | Faculty of Applied Science & Engineering | This course will teach students how to build mathematical models of dynamic systems and how to use these models for prediction and control purposes. The course will deal primarily with a system identification approach to modelling (using observations from the system to build a model). Both continuous time and discrete time representations will be treated along with deterministic and stochastic models. This course will make extensive use of interactive learning by having students use computer based tools available in the Matlab software package (e.g. the System Identification Toolbox and the Model Predictive Control Toolbox). |
| APS360H1 | Artificial Intelligence Fundamentals | St. George | Cross Disciplinary Programs Office | Faculty of Applied Science & Engineering | A basic introduction to the history technology programming and applications of artificial intelligence with emphasis on fast evolving field of machine learning. Topics to be covered may include linear regression logistic regression support vector machines and neural networks. An applied approach will be taken where students get hands-on exposure to AI techniques through the use of state-of-the-art machine learning software frameworks. |
| ECE324H1 | Introduction to Machine Intelligence | St. George | Division of Engineering Science | Faculty of Applied Science & Engineering | This course will provide students with an introduction to machine learning engineering as a software and engineering discipline. It focuses on the neural network method. Lectures will cover the basic mathematics and intuitions behind neural networks in particular deep convolutional neural networks and their application as classifiers and predictions using regression. There will be a focus on conveying known methods to make neural network training succeed. Other topics may include Natural Language Processing basics recurrent neural networks transfer learning and generative adversarial networks. There will be reflection on ethics in machine learning. A significant component of the course will be hands-on exposure to a machine-learning software framework culminating in a design project. |
| ROB311H1 | Artificial Intelligence | St. George | Division of Engineering Science | Faculty of Applied Science & Engineering | An introduction to the fundamental principles of artificial intelligence from a mathematical perspective. The course will trace the historical development of AI and describe key results in the field. Topics include the philosophy of AI search methods in problem solving knowledge representation and reasoning logic planning and learning paradigms. A portion of the course will focus on ethical AI embodied AI and on the quest for artificial general intelligence. |
| ROB313H1 | Introduction to Learning from Data | St. George | Division of Engineering Science | Faculty of Applied Science & Engineering | This course will introduce students to the topic of machine learning which is key to the design of intelligent systems and gaining actionable insights from datasets that arise in computational science and engineering. The course will cover the theoretical foundations of this topic as well as computational aspects of algorithms for unsupervised and supervised learning. The topics to be covered include: The learning problem clustering and k-means principal component analysis linear regression and classification generalized linear models bias-variance tradeoff regularization methods maximum likelihood estimation kernel methods the representer theorem radial basis functions support vector machines for regression and classification an introduction to the theory of generalization feedforward neural networks stochastic gradient descent ensemble learning model selection and validation. |
| *This course is pending approval by Faculty Council for the 2018-19 academic year/" | |||||
| ECE368H1 | Probabilistic Reasoning | St. George | Edward S. Rogers Sr. Dept. of Electrical & Computer Engin. | Faculty of Applied Science & Engineering | This course will focus on different classes of probabilistic models and how based on those models one deduces actionable information from data. The course will start by reviewing basic concepts of probability including random variables and first and second-order statistics. Building from this foundation the course will then cover probabilistic models including vectors (e.g. multivariate Gaussian) temporal (e.g. stationarity and hidden Markov models) and graphical (e.g. factor graphs). On the inference side topics such as hypothesis testing marginalization estimation and message passing will be covered. Applications of these tools cover a vast range of data processing domains including machine learning communications search recommendation systems finance robotics and navigation. |
| ECE421H1 | Introduction to Machine Learning | St. George | Edward S. Rogers Sr. Dept. of Electrical & Computer Engin. | Faculty of Applied Science & Engineering | An Introduction to the basic theory the fundamental algorithms and the computational toolboxes of machine learning. The focus is on a balanced treatment of the practical and theoretical approaches along with hands on experience with relevant software packages. Supervised learning methods covered in the course will include: the study of linear models for classification and regression neural networks and support vector machines. Unsupervised learning methods covered in the course will include: principal component analysis k-means clustering and Gaussian mixture models. Theoretical topics will include: bounds on the generalization error bias-variance tradeoffs and the Vapnik-Chervonenkis (VC) dimension. Techniques to control overfitting including regularization and validation will be covered. |
| MIE231H1 | Probability and Statistics with Engineering Applications | St. George | Mechanical & Industrial Engineering | Faculty of Applied Science & Engineering | Use of data in engineering decision processes. Elements of probability theory. Discrete and continuous random variables. Standard distributions: binomial Poisson hypergeometric exponential normal etc. Expectation and variance. Random sampling and parameter estimation. Confidence intervals. Hypothesis testing. Goodness-of-fit tests. Regression and correlation. Statistical Process Control and quality assurance. Engineering applications in manufacturing instrumentation and process control. |
| MIE236H1 | Probability | St. George | Mechanical & Industrial Engineering | Faculty of Applied Science & Engineering | Introduction to probability (the role of probability exploratory data analysis and basic graphical methods). Sample space and events Venn diagram. Definitions of probability. Axiomatic definition and basic rules. Conditional probability and Bayes' rule. Concept of random variables. Discrete continuous and joint distributions. Probability mass functions density function cumulative distribution function. Expectation variance and covariance. Important discrete and continuous distributions. Multivariate normal distribution. Functions of random variables. Moment Generating functions. Central limit theorem laws of large numbers Markov and Chebyshev's inequalities types of convergence. Fundamental sampling distributions Chi-square t and F distributions. One sample estimation and hypothesis testing. |
| MIE368H1 | Analytics in Action | St. George | Mechanical & Industrial Engineering | Faculty of Applied Science & Engineering | This course showcases the impact of analytics focusing on real world examples and case studies. Particular focus on decision analytics where data and models are combined to ultimately improve decision-making. Methods include: linear and logistic regression classification and regression trees clustering linear and integer optimization. Application areas include: healthcare business sports manufacturing finance transportation public sector. |
| CSC311H1 | Introduction to Machine Learning | St. George | Computer Science | Faculty of Arts and Science | An introduction to methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods decision trees linear models and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy. Basics of reinforcement learning. |
| CSC384H1 | Introduction to Artificial Intelligence | St. George | Computer Science | Faculty of Arts and Science | Theories and algorithms that capture (or approximate) some of the core elements of computational intelligence. Topics include: search; logical representations and reasoning classical automated planning representing and reasoning with uncertainty learning decision making (planning) under uncertainty. Assignments provide practical experience in both theory and programming of the core topics. |
| CSC412H1 | Probabilistic Learning and Reasoning | St. George | Computer Science | Faculty of Arts and Science | An introduction to probability as a means of representing and reasoning with uncertain knowledge. Qualitative and quantitative specification of probability distributions using probabilistic graphical models. Algorithms for inference and probabilistic reasoning with graphical models. Statistical approaches and algorithms for learning probability models from empirical data. Applications of these models in artificial intelligence and machine learning. |
| PHY408H1 | Time Series Analysis | St. George | Physics | Faculty of Arts and Science | The analysis of digital sequences; filters; the Fourier Transform; windows; truncation effects; aliasing; auto and cross-correlation; stochastic processes power spectra; least squares filtering; application to real data series and experimental design. |
| POL232H1 | Introduction to Quantitative Reasoning II | St. George | Political Science | Faculty of Arts and Science | Building up on POL222H1 students will continue to build theoretical foundations of quantitative empirical research such as probability theory and statistical inference. They will also learn the basic use of statistical software and have become able to conduct a basic data analysis by themselves by the end of semester. |
| PSY202H1 | Statistics II | St. George | Psychology | Faculty of Arts and Science | Fundamentals of statistical analysis of experimental and observational data including linear models the analysis of variance a priori contrasts post-hoc tests power analysis and effect size calculations. |
| ACT452H1 | Loss Models II | St. George | Statistical Sciences | Faculty of Arts and Science | Estimation of Loss and Survival Models using complete censored and truncated data. Product-Limit estimation empirical estimation moment and percentile estimation maximum likelihood estimation and simulation models. |
| JSC270H1 | Data Science I | St. George | Statistical Sciences | Faculty of Arts and Science | This course is restricted to students in the Data Science Specialist program. Data exploration and preparation; data visualization and presentation; and computing with data will be introduced. Professional skills such as oral and written communication and ethical skills for data science will be introduced. Data science workflows will be integrated throughout the course. These topics will be explored through case studies and collaboration with researchers in other fields. |
| JSC370H1 | Data Science II | St. George | Statistical Sciences | Faculty of Arts and Science | This course is restricted to students in the Data Science Specialist program. Students will learn to identify and answer questions through the application of exploratory data analysis data visualization statistical methods or machine learning algorithms to complex data. Software development for data science and reproducible workflows. Communication of statistical information at various technical levels ethical practice of data analysis and software development and teamwork skills. Topics will be explored through case studies and collaboration with researchers in other fields. |
| JSC470H1 | Data Science III | St. George | Statistical Sciences | Faculty of Arts and Science | This course is restricted to students in the Data Science Specialist program. Research topics and applications of data science methods will be explored through case studies and collaboration with researchers in other fields. Data analysis visualization and communication of statistical information at various technical levels ethical practice of data analysis and software development and teamwork skills. |
| STA130H1 | An Introduction to Statistical Reasoning and Data Science | St. George | Statistical Sciences | Faculty of Arts and Science | This course intended for students considering a program in Statistical Sciences discusses the crucial role played by statistical reasoning in solving challenging problems from natural science social science technology health care and public policy using a combination of logical thinking mathematics computer simulation and oral and written discussion and analysis. |
| STA220H1 | The Practice of Statistics I | St. George | Statistical Sciences | Faculty of Arts and Science | An introductory course in statistical concepts and methods emphasizing exploratory data analysis for univariate and bivariate data sampling and experimental designs basic probability models estimation and tests of hypothesis in one-sample and comparative two-sample studies. A statistical computing package is used but no prior computing experience is assumed. Note: STA220H1does not count as a distribution requirement course. |
| STA221H1 | The Practice of Statistics II | St. George | Statistical Sciences | Faculty of Arts and Science | Continuation of STA220H1 (or similar course) emphasizing major methods of data analysis such as analysis of variance for one factor and multiple factor designs regression models categorical and non-parametric methods (Note: STA221H1 does not count as a distribution requirement course). |
| STA237H1 | Probability Statistics and Data Analysis I | St. George | Statistical Sciences | Faculty of Arts and Science | An introduction to probability using simulation and mathematical frameworks with emphasis on the probability needed for more advanced study in statistical practice. Topics covered include probability spaces random variables discrete and continuous probability distributions probability mass density and distribution functions expectation and variance independence conditional probability the law of large numbers the central limit theorem sampling distributions. Computer simulation will be taught and used extensively for calculations and to guide the theoretical development. |
| STA238H1 | Probability Statistics and Data Analysis II | St. George | Statistical Sciences | Faculty of Arts and Science | An introduction to statistical inference and practice. Statistical models and parameters estimators of parameters and their statistical properties methods of estimation confidence intervals hypothesis testing likelihood function the linear model. Use of statistical computation for data analysis and simulation. |
| STA302H1 | Methods of Data Analysis I | St. George | Statistical Sciences | Faculty of Arts and Science | Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression. |
| STA303H1 | Methods of Data Analysis II | St. George | Statistical Sciences | Faculty of Arts and Science | Analysis of variance for one-and two-way layouts logistic regression loglinear models longitudinal data introduction to time series. |
| STA304H1 | Surveys Sampling and Observational Data | St. George | Statistical Sciences | Faculty of Arts and Science | Design of surveys sources of bias randomized response surveys. Techniques of sampling; stratification clustering unequal probability selection. Sampling inference estimates of population mean and variances ratio estimation. Observational data; correlation vs. causation missing data sources of bias. |
| STA313H1 | Data Visualization | St. George | Statistical Sciences | Faculty of Arts and Science | An introduction to data visualization and the use of visual and interactive representations of data to support human cognition. This course covers visualization techniques and algorithms based on principles from graphic design perceptual psychology cognitive science and human-computer interaction. Topics include: graphic design interaction perception and cognition communication and ethics. Computational tutorials involve design review implementation and testing of information visualizations. |
| STA314H1 | Statistical Methods for Machine Learning I | St. George | Statistical Sciences | Faculty of Arts and Science | Statistical methods for supervised and unsupervised learning from data: training error test error and cross-validation; classification regression and logistic regression; principal components analysis; stochastic gradient descent; decision trees and random forests; k-means clustering and nearest neighbour methods. Computational tutorials will support the efficient application of these methods. |
| STA365H1 | Applied Bayesian Statistics | St. George | Statistical Sciences | Faculty of Arts and Science | Bayesian inference has become an important applied technique and is especially valued to solve complex problems. This course first examines the basics of Bayesian inference. From there this course looks at modern computational methods and how to make inferences on complex data problems. |
| STA410H1 | Statistical Computation | St. George | Statistical Sciences | Faculty of Arts and Science | Programming in an interactive statistical environment. Generating random variates and evaluating statistical methods by simulation. Algorithms for linear models maximum likelihood estimation and Bayesian inference. Statistical algorithms such as the Kalman filter and the EM algorithm. Graphical display of data. |
| STA414H1 | Statistical Methods for Machine Learning II | St. George | Statistical Sciences | Faculty of Arts and Science | Probabilistic foundations of supervised and unsupervised learning methods such as naive Bayes mixture models and logistic regression. Gradient-based fitting of composite models including neural nets. Exact inference stochastic variational inference and Marko chain Monte Carlo. Variational autoencoders and generative adversarial networks. |
| STA437H1 | Methods for Multivariate Data | St. George | Statistical Sciences | Faculty of Arts and Science | Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and partial multiple and canonical correlations; multivariate analysis of variance; profile analysis and curve fitting for repeated measurements; classification and the linear discriminant function. |
| STA442H1 | Methods of Applied Statistics | St. George | Statistical Sciences | Faculty of Arts and Science | Advanced topics in statistics and data analysis with emphasis on applications. Diagnostics and residuals in linear models introduction to generalized linear models graphical methods additional topics such as random effects models designed experiments model selection analysis of censored data introduced as needed in the context of case studies. |
| STA452H1 | Mathematical Statistics I | St. George | Statistical Sciences | Faculty of Arts and Science | Statistical theory and its applications at an advanced mathematical level. Topics include probability and distribution theory as it specifically pertains to the statistical analysis of data. Linear models and the geometry of data least squares and the connection to conditional expectation. The basic concept of inference and the likelihood function. |
| STA457H1 | Time Series Analysis | St. George | Statistical Sciences | Faculty of Arts and Science | An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods filtering and smoothing time series theory of stationary processes identification and estimation of time series models forecasting seasonal adjustment spectral estimation bivariate time series models. |
| ARC384H1 | Simulation and Data Visualization | St. George | John H. Daniels Faculty of Architecture Landscape & Design | John H. Daniels Faculty of Architecture Landscape & Design | An exploration of the various simulation software programs available to designers this course will introduce the theory of simulation and discuss the history of its use in science generally and in architecture landscape architecture and urbanism specifically. |
Specialist A/AI courses
| Code | Course Name | Campus | Department | School | Description |
|---|---|---|---|---|---|
| ANT200H5 | Introduction to the Practice of Archaeology | Mississauga | Anthropology | University of Toronto Mississauga | Archaeological theory method and technique. Principles of scientific research will be applied to archaeological information. The course will cover the following topics: how archaeology applies the scientific method; how archaeological projects are planned and organized; how archaeological data are recovered through survey excavation and other means; how archaeological data are organized and analyzed to produce information about the human past; the major theoretical paradigms that archaeologists use to interpret the human past. [24L 12P] |
| ANT312H5 | Archaeological Analysis | Mississauga | Anthropology | University of Toronto Mississauga | This course will introduce the process of archaeological research from project design through report write-up. The student will create a project proposal choose methods of survey and excavation describe and organize data for analysis and summarize findings in a project report. [12L 24P] |
| ANT338H5 | Laboratory Methods in Biological Anthropology | Mississauga | Anthropology | University of Toronto Mississauga | Recommended for those who may specialize in biological anthropology. Students will be introduced to the process of conducting research including selected laboratory procedures and how they are used to generate and/or analyze data. Students conduct anthropometric assessment of growth and body size nutrition assessment through 24-hour dietary recall and assessment of physical activity and sleep using triaxial accelerometry. These biometric techniques have numerous applications in both research and clinical settings. Students in this course will develop applied skills in bioanthropological assessment that can be used in the fields of anthropology population health public health nutrition and human development. [12L 24P] |
| ANT358H5 | Field Methods in Sociocultural Anthropology | Mississauga | Anthropology | University of Toronto Mississauga | This course investigates how sociocultural and/or linguistic anthropologists collect data conduct fieldwork and interpret research results. The course will benefit students who want to gain an appreciation of research design and practice and those considering graduate-level work in anthropology or another social science. [24L] |
| ANT407H5 | Quantitative Methods in Archaeology and Biological Anthropology | Mississauga | Anthropology | University of Toronto Mississauga | This course will provide students with the basic analytic background necessary to evaluate quantitative data in biological anthropology and archaeology. Students will be introduced to foundational statistical concepts and research methods suitable for anthropological exploration. The focus will be on analysing univariate and bivariate data using both nonparametric and parametric statistical techniques hypothesis testing and methods of data collection. The goal of this course is for students to learn how to manipulate simple datasets ask and answer theoretically relevant questions and choose the appropriate statistical test for a given research problem. Students will receive hands-on training during lab components and will learn how to analyse data using relevant statistical software. Students will have access to a number of biological anthropology and archaeology datasets for class assignments. No prior knowledge of statistics and mathematics is required. [24L 12P] |
| BIO313H5 | Field Methods and Experimental Design in Ecology | Mississauga | Biology | University of Toronto Mississauga | This course will provide Biology Majors and Specialists particularly interested in ecology with integrated practical exposure to field and laboratory research methods on plant animal and microbial communities including study design data collection statistical analysis and interpretation of results. [36P] |
| BIO362H5 | Bioinformatics | Mississauga | Biology | University of Toronto Mississauga | Bioinformatics uses and develops computational tools to understand biological processes from the level of single molecules to whole genomes and organisms. The biotechnology revolution has meant that bioinformatics is now used in many cutting edge biological research areas from medicine to phylogenetics. This course will introduce core concepts practices and research topics including DNA/Protein alignment DNA sequence analysis interacting with scientific databases and genome sequencing technology. This course includes computer-based practicals wherein students will apply bioinformatic tools and be introduced to basic computer programming - no previous experience is required. [12L 36P] |
| CHM211H5 | Fundamentals of Analytical Chemistry | Mississauga | Chemical and Physical Sciences | University of Toronto Mississauga | A rigorous introduction to the theory and practice of analytical chemistry. Development and applications of basic statistical concepts in treatment and interpretation of analytical data; direct and indirect precipitations; volumetric methods; acid-base complexometric redox and precipitation titrations; introduction to instrumental methods; potentiometry and absorption spectroscopy. Applications in biomedical forensic and environmental areas will be considered. [24L 48P 12T] |
| PHY324H5 | Advanced Physics Laboratory | Mississauga | Chemical and Physical Sciences | University of Toronto Mississauga | A modular practical course that develops the experimental and computational skills necessary to get deeper insight in physical phenomena. Selected physics experiments and modeling that illustrate important principles of physics are applied: Experimental measurements and skills data and uncertainty analysis mathematical models computational simulations and solutions. [48P] |
| PHY351H5 | Climate Physics | Mississauga | Chemical and Physical Sciences | University of Toronto Mississauga | This course presents the physics of EarthÕs climate. Emphasis will be placed on the basic principles and processes involved in physical and dynamic climatology and the physical interactions between the atmosphere oceans and land surface. Topics may include components of the climate system and global energy balance atmospheric radiative transfer surface energy balance the hydrological cycle general circulation of the atmosphere ocean circulation and climate climate modeling and climate change. In the lab practicals students will gain hands-on experience in analyzing climate data and simple climate modeling. [24L 24P] |
| ECO220Y5 | Introduction to Data Analysis and Applied Econometrics | Mississauga | Economics | University of Toronto Mississauga | An introduction to the use of statistical analysis including such topics as elementary probability theory sampling distributions tests of hypotheses estimation; analysis of variance and regression analysis. Emphasis is placed on applications in economics and business problems. [48L 24P] |
| ECO375H5 | Applied Econometrics I | Mississauga | Economics | University of Toronto Mississauga | (Formerly ECO327Y5) This course is an introduction to econometrics. Statistical foundations and the interpretation of multiple regression models with an emphasis on cross-sectional data. Application of regressions to a wide variety of economic questions and data sources including the use of statistical software. Problems in the identification of causality and an introduction to methods of addressing common statistical issues. This course is recommended for students contemplating graduate studies. [24L 24P] This course is part of the Certificate in Advanced Economics. |
| ECO383H5 | Introduction to Empirical Methods of Microeconomics | Mississauga | Economics | University of Toronto Mississauga | Formerly: Economics of Education) For students who would like to learn more about economics data analysis - this course provides an intuitive introduction to empirical methods in microeconomics. The class begins with a self-contained and intuitive treatment of modern methods used in microeconomic data analysis. We then go on to study some interesting current empirical research focusing on the education field to see how those methods are applied. The course should prepare you to read current empirical research in microeconomics -- without any preparation empirical papers can seem rather impenetrable. This course serves as a complement to and a foundation for 'Applied Econometrics I' (ECO375H5). [24L] |
| ECO456H5 | Public Policy Analysis | Mississauga | Economics | University of Toronto Mississauga | (Formerly ECO356H5) This course provides an opportunity for students to work with real-world data to address current public policy questions. The course discusses issues that arise when analyzing non-experimental social science data and will teach students to recognize the types of research designs that can lead to convincing policy conclusions. A hands-on approach will be emphasized. [24L] |
| ECO466H5 | Empirical Macroeconomics and Policy | Mississauga | Economics | University of Toronto Mississauga | Students will increase their data literacy and learn how to apply techniques to address policy issues. The topics covered will include the practical design of monetary policy the rationale of current monetary policy in Canada and statistical methods for predicting key macroeconomic variables. As part of the course students will follow current global issues and will forecast how the domestic and international events may alter the Bank of Canada's monetary policy in the short run. This course builds on material covered in ECO202Y5/ECO208Y5/ECO209Y5 ECO325H5 and ECO375H5. |
| ECO475H5 | Applied Econometrics II | Mississauga | Economics | University of Toronto Mississauga | (Formerly 327Y5) A research-oriented course continuing from ECO375H. The regression model is extended in several possible directions: time series analysis; panel data techniques; instrumental variables; simultaneous equations; limited dependent variables. Students will complete a major empirical term paper applying the tools of econometrics to a topic chosen by the student. [24L 24P] |
| GGR276H5 | Spatial Data Science I | Mississauga | Geography Geomatics and Environment | University of Toronto Mississauga | Introduction to the study of geographical phenomena using descriptive and inferential statistics. Fundamentals of geographic data and statistical problem solving using non-spatial and spatial descriptive statistics. Decision making using evidence gathered from inferential statistical analysis. Graphical summary geographic visualization and mapping of analytical results. Application of state of the art software for statistical analysis. Provides background for future studies in geographic information systems and advanced statistical analysis. The course strikes a balance between developing an understanding of core non-spatial and spatial statistical concepts while demonstrating technical proficiency in the application of software to the study of geographical questions. [24L 12P] |
| GGR277H5 | Social Research Methods in Geography | Mississauga | Geography Geomatics and Environment | University of Toronto Mississauga | This course introduces students to the range of social research methods and approaches used in the field of human geography. The course will cover research design research ethics data collection methods including interviews focus groups surveys etc. ethics in conducting research with human subjects and data analysis and interpretation. This course fulfills 1 field day. [24L 12T] |
| GGR278H5 | Geographical Information Systems | Mississauga | Geography Geomatics and Environment | University of Toronto Mississauga | Introduction to models of representation and management of geographical data for scientific analysis. Basic quantitative methods and techniques for geographic data analysis including collection manipulation description and interpretation. Practical exercises using GIS and statistical software packages with examples drawn from both physical and human geography. [24L 12P] |
| GGR335H5 | GIS and Remote Sensing Integration | Mississauga | Geography Geomatics and Environment | University of Toronto Mississauga | The integration of GIS and remote sensing is at the center of a larger trend toward the fusion of different kinds of geospatial data and technologies. The purpose of this course is to familiarize students with the various ways in which GIS and remote sensing have been integrated and used for environmental applications at a range of spatial and temporal scales. A part of the course will be devoted to application projects employing remote sensing and/or GIS data analysis in natural resources and environmental assessments. [24L 24P] |
| GGR376H5 | Spatial Data Science II | Mississauga | Geography Geomatics and Environment | University of Toronto Mississauga | This course builds on quantitative methods introduced in GGR276 and aims to provide a broad study of advanced statistical methods and their use in a spatial context in physical social and environmental sciences. The course covers theories methods and applications geared towards helping students develop an understanding of the important theoretical concepts in spatial data analysis and gain practical experience in application of spatial statistics to a variety of physical social and environmental problems using advanced statistical software. [24L 24P] |
| CCT208H5 | Communications Research Methods | Mississauga | Institute of Communication and Culture | University of Toronto Mississauga | This course is a critical survey of research methodologies in the field of communication and media. A central goal of the course is to train students to collect manage analyze and interpret social science research data. Each week students are required to attend a one hour in-class lecture and view a one hour online lecture. The online lectures will be posted at least one week before the week in which they are assigned. [24L 8T] |
| MGT270H5 | Data Analytics for Management | Mississauga | Management | University of Toronto Mississauga | (Formerly MGM301H5). Students will be introduced to a variety of techniques for analyzing data for the purposes of decision and control. Topics covered include mathematical modelling decision analysis and operations management. [24L 12P] |
| MGT458H5 | Big Data and Marketing Analysis | Mississauga | Management | University of Toronto Mississauga | Recent advances in computer technology have led to an explosion in the amount of data available for companies to use for market research. In order to be effective as a marketing manager today it is necessary to understand how to apply cutting edge statistical models to large databases such as scanner data loyalty program data or internet marketing data and to be able to obtain managerial insights from model results. This course will introduce students to marketing analytics driven by big data using applications from real world business problems. [24P] |
| CSC333H5 | Forensic Computing | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | Introduction to the tools and techniques of the digital detective. Electronic discovery of digital data including field investigation methods of the computer crime scene. Focus on the computer science behind computer forensics network forensics and data forensics. Forensic topics include: computer structure data acquisition from storage media file system analysis network intrusion detection electronic evidence Canadian computer crime case law. [24L 12P] |
| CSC338H5 | Numerical Methods | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | Computational methods for solving numerical problems in science engineering and business. Linear and non-linear equations approximation optimization interpolation integration and differentiation. The aim is to give students a basic understanding of floating-point arithmetic and the implementation of algorithms used to solve numerical problems as well as a familiarity with current numerical computing environments.Course concepts are crucial to a wide range of practical applications such as computational finance and portfolio management graphics and special effects data mining and machine learning as well as robotics bioinformatics medical imaging and others. [24L 12T] |
| STA218H5 | Statistics for Management | Mississauga | Mathematical and Computational Sciences | University of Toronto Mississauga | Acquaints students with the statistical principles that managers need in order to extract information from numerical data and to understand the formal principles of decision-making under conditions of uncertainty. Covers descriptive statistics elementary probability expected values sampling distributions point and interval estimation hypothesis testing for normal and binomial data and multiple regression analysis. [36L 12T] |
| PSY309H5 | Experimental Design and Theory | Mississauga | Psychology | University of Toronto Mississauga | Practical problems in research design and interpretation of experimental findings. Practice in the critical evaluation of research findings. Students will gain experience in the processes involved in collecting and analyzing data and in using computers to set up psychological experiments. [36P] |
| ANTC67H3 | Foundations in Epidemiology | Scarborough | Anthropology (UTSC) | University of Toronto Scarborough | Epidemiology is the study of disease and its determinants in populations. It is grounded in the biomedical paradigm statistical reasoning and that risk is context specific. This course will examine such issues as: methods of sampling types of controls analysis of data and the investigation of epidemics.Science credit |
| BIOB12H3 | Cell and Molecular Biology Laboratory | Scarborough | Biological Sciences (UTSC) | University of Toronto Scarborough | A practical introduction to experimentation in cell and molecular biology. Lab modules will introduce students to concepts and techniques in the general preparation of solutions and buffers microbiology molecular biology biochemistry microscopy data analysis and science communication. This core laboratory course is the gateway for Molecular Biology & Biotechnology Specialists to upper level laboratory offerings. |
| ACMB02H3 | Methods of Inquiry and Investigation for ACM Programs | Scarborough | Dept. of Arts Culture & Media (UTSC) | University of Toronto Scarborough | An introduction to investigative research methods where the humanities and social sciences meet including visual documentary ethnographic interview and other qualitative tools for analyzing social and cultural practices. Students develop skills to identify research inquiries formulate approaches to investigate locate collect and learn from data analyze evidence and communicate results. |
| JOUB19H3 | Data Management and Presentation | Scarborough | Dept. of Arts Culture & Media (UTSC) | University of Toronto Scarborough | To develop stories from raw numbers students will navigate spreadsheets and databases and acquire raw data from web pages. Students will learn to use Freedom of Information requests to acquire data. This course is taught at Centennial College and is open only to students in the Specialist (Joint) program in Journalism. |
| VPSC70H3 | Theory and Practice: New Media in Studio | Scarborough | Dept. of Arts Culture & Media (UTSC) | University of Toronto Scarborough | Information technologies are radically and rapidly transforming our culture. Networking robotics GPS ubiquitous computing data mining rfid biotech surveillance sound installation digital image processing and interactive display are all offering new opportunities for the artist as well as new critical issues to address. Students will create affordable projects that address these issues. |
| CSCD18H3 | Computer Graphics | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | The course will cover in detail the principles and algorithms used to generate high-quality computer generated images for fields as diverse as scientific data visualization modeling computer aided design human computer interaction special effects and video games. Topics covered include image formation cameras and lenses object models object manipulation transformations illumination appearance modeling and advanced rendering via ray-tracing and path-tracing. Throughout the course students will implement a working rendering engine in a suitable programming language. |
| STAD70H3 | Statistics and Finance II | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | A survey of statistical techniques used in finance. Topics include mean-variance and multi-factor analysis simulation methods for option pricing Value-at-Risk and related risk-management methods and statistical arbitrage. A computer package will be used to illustrate the techniques using real financial data. |
| EESC03H3 | Geographic Information Systems and Remote Sensing | Scarborough | Dept. of Physical & Environmental Sci (UTSC) | University of Toronto Scarborough | This course focuses on the use of Geographic Information Systems (GIS) and Remote Sensing (RS) for solving a range of scientific problems in the environmental sciences and describing their relationship with - and applicability to - other fields of study (e.g. geography computer science engineering geology ecology and biology). Topics include (but are not limited to): spatial data types formats and organization; geo-referencing and coordinate systems; remotely sensed image manipulation and analysis; map production. |
| EESC16H3 | Field Camp I | Scarborough | Dept. of Physical & Environmental Sci (UTSC) | University of Toronto Scarborough | Many environmental problems can only be assessed by collecting geological and other environmental data in the field. This course will provide students with the necessary skills for fieldwork investigations in a range of environments. The camp is held annually either in May or late August. Locations for the camp include Costa Rica Rockies Arizona and Appalachians. |
| EESD06H3 | Climate Change Impact Assessment | Scarborough | Dept. of Physical & Environmental Sci (UTSC) | University of Toronto Scarborough | Climate change over the last 150 years is reviewed by examining the climate record using both direct measurements and proxy data. Projection of future climate is reviewed using the results of sophisticated climate modeling. The climate change impact assessment formalism is introduced and applied to several examples. Students will acquire practical experience in climate change impact assessment through case studies. |
| EESD21H3 | Geophysical and Climate Data Analysis | Scarborough | Dept. of Physical & Environmental Sci (UTSC) | University of Toronto Scarborough | This course offers an advanced introduction to geophysical data analysis. It is intended for upper-level undergraduate students and graduate students interested in data analysis and statistics in the geophysical sciences and is mainly laboratory (computer) based. The goal is to provide an understanding of the theory underlying the statistical analysis of geophysical data in space time and spectral domains and to provide the tools to undertake this statistical analysis. Important statistical techniques such as regression correlation and spectral analysis of time series will be explored with a focus on hypothesis formulation and interpretation of the analysis. Multivariate approaches will also be introduced. Although some previous knowledge of probability and statistics will be helpful a review will be provided at the beginning of the course. Concepts and notation will be introduced as needed.Jointly offered with EES1132H. |
| GGRA30H3 | Geographic Information Systems (GIS) and Empirical Reasoning | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | Confirmatory causal modeling and GIS; map as model; GIS data input; cartographic and GIS data structures; data errors and editing; elementary spatial analysis; measurement; map comparison; classification; statistical surfaces; spatial arrangement; privacy issues. |
| GGRB30H3 | Fundamentals of GIS I | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | This course provides a practical introduction to digital mapping and spatial analysis using a geographic information system (GIS). The course is designed to provide hands-on experience using GIS to analyse spatial data and create maps that effectively communicate data meanings. Students are instructed in GIS methods and approaches that are relevant not only to Geography but also to many other disciplines. In the lectures we discuss mapping and analysis concepts and how you can apply them using GIS software. In the practice exercises and assignments you then learn how to do your own data analysis and mapping gaining hands-on experience with ArcGIS software the most widely used GIS software. |
| GGRB32H3 | Fundamentals of GIS II | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | This course builds on GGRB30 Fundamentals of GIS continuing the examination of theoretical and analytical components of GIS and spatial analysis and their application through lab assignments. The course covers digitizing topology vector data models remote sensing and raster data models and analysis geoprocessing map design and cartography data acquisition metadata and data management and web mapping. |
| GGRC30H3 | Advanced GIS | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | This course covers advanced theoretical and practical issues of using GIS systems for research and spatial analysis. Students will learn how to develop and manage GIS research projects create and analyze three-dimensional surfaces build geospatial models visualize geospatial data and perform advanced spatial analysis. Lectures introduce concepts and labs implement them. |
| GGRC32H3 | Essential Spatial Analysis | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | This course builds on introductory statistics and GIS courses by introducing students to the core concepts and methods of spatial analysis. With an emphasis on spatial thinking in an urban context topics such as distance decay distance metrics spatial interaction spatial distributions and spatial autocorrelation will be used to quantify spatial patterns and identify spatial processes. These tools are the essential building blocks for the quantitative analysis of urban spatial data.Area of focus: Urban Geography |
| GGRC34H3 | Crowd-sourced Urban Geographies | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | Significant recent transformations of geographic knowledge are being generated by the ubiquitous use of smartphones and other distributed sensors while web-based platforms such as Open Street Map and Public Participation GIS (PPGIS) have made crowd-sourcing of geographical data relatively easy. This course will introduce students to these new geographical spaces approaches to creating them and the implications for local democracy and issues of privacy they pose.Area of focus: Urban Geography |
| GGRD30H3 | GIS Research Project | Scarborough | Human Geography (UTSC) | University of Toronto Scarborough | Students will design manage and complete a research project using GIS. Students will work in teams of 4-6 to pose a research question acquire a dataset and organize and analyze the data to answer their question. The course will teach research design project management data analysis team work and presentation of final results. |
| MGFD25H3 | Financial Technologies and Applications (FinTech) | Scarborough | Management (UTSC) | University of Toronto Scarborough | Financial Technologies (FinTech) are changing our everyday lives and challenging many financial institutions to evolve and adapt. The course explores disruptive financial technologies and innovations such as mobile banking cryptocurrencies Robo-advisory and the financial applications of artificial intelligence (AI) etc. The course covers the various areas within the financial industry that are most disrupted thus leading to discussions on the challenges and opportunities for both the financial institutions and the regulators. Classes are conducted in the experiential learning lab where students explore academic research and practical components of FinTech. |
| MGMC01H3 | Market Research | Scarborough | Management (UTSC) | University of Toronto Scarborough | A decision oriented course which introduces students to the market research process. It covers different aspects of marketing research both quantitative and qualitative and as such teaches some essential fundamentals for the students to master in case they want to specialize in marketing. And includes alternative research approaches (exploratory descriptive causal) data collection sampling analysis and evaluation procedures are discussed. Theoretical and technical considerations in design and execution of market research are stressed. Instruction involves lectures and projects including computer analysis. |
| MGMD01H3 | Applied Marketing Models | Scarborough | Management (UTSC) | University of Toronto Scarborough | Marketing is a complex discipline incorporating not only an “art†but also a “scienceâ€. This course reviews the “science†side of marketing by studying multiple models used by companies. Students will learn how to assess marketing problems and use appropriate models to collect analyze and interpret marketing data. |
| MGOD30H3 | Business Data Analytics | Scarborough | Management (UTSC) | University of Toronto Scarborough | The course lays the foundation for big data analysis and predictive analytics via state-of-the-art methodologies and computational tools and incorporates hands-on case studies. By the end of the course students will be able to develop data architecture plans to improve decision making in business processes. |
| MGSD15H3 | Managing in the Information Economy | Scarborough | Management (UTSC) | University of Toronto Scarborough | Topics include identifying managing and exploiting information assets the opportunities and limits of dealing with Big Data the impact of digitalization of information managing under complexity globalization and the rise of the network economy. Students will explore a topic in greater depth through the writing of a research paper. |
| POLC11H3 | Applied Statistics for Politics and Public Policy | Scarborough | Political Science (UTSC) | University of Toronto Scarborough | In this course students learn to apply data analysis techniques to examples drawn from political science and public policy. Students will learn to complete original analyses using quantitative techniques commonly employed by political scientists to study public opinion and government policies. Rather than stressing mathematical concepts the emphasis of the course will be on the application and interpretation of the data as students learn to communicate their results through papers and/or presentations. |
| PSYB03H3 | Introduction to Computers in Psychological Research | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | The course will provide introductory knowledge and hands-on training in computer-based implementations of experimental design data processing and result interpretation in psychology. The course covers implementations of experimental testing paradigms computational explorations of empirical data structure and result visualization with the aid of specific programming tools (e.g. Matlab). |
| PSYB07H3 | Data Analysis in Psychology | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | This course focuses on the fundamentals of the theory and the application of statistical procedures used in research in the field of psychology. Topics will range from descriptive statistics to simple tests of significance such as Chi-Square t-tests and one-way Analysis-of-Variance. A working knowledge of algebra is assumed. Students in the Specialist programs in Psychology Psycholinguistics or Neuroscience will be given priority for this course. |
| PSYC03H3 | Computers in Psychological Research: Advanced Topics | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | The course will provide advanced knowledge and hands-on training in computer-based implementations of experimental design data processing and result interpretation in psychology. The course covers implementations of experimental testing paradigms computational explorations of empirical data structure and result visualization with the aid of specific programming tools (e.g. Matlab). |
| PSYC08H3 | Advanced Data Analysis in Psychology | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | The primary focus of this course is on the understanding of Analysis-of-Variance and its application to various research designs. Examples will include a priori and post hoc tests. Finally there will be an introduction to multiple regression including discussions of design issues and interpretation problems. |
| PSYC09H3 | Applied Multiple Regression in Psychology | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | An introduction to multiple regression and its applications in psychological research. The course covers the data analysis process from data collection to interpretation: how to deal with missing data the testing of assumptions addressing problem of multicolinearity significance testing and deciding on the most appropriate model. Several illustrative data sets will be explored in detail. The course contains a brief introduction to factor analysis. The goal is to provide the students with the skills and understanding to conduct and interpret data analysis in non-experimental areas of psychology. |
| PSYC75H3 | Cognitive Psychology Laboratory | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | This course introduces conceptual and practical issues concerning research in cognitive psychology. Students will be introduced to current research methods through a series of practical exercises conducted on computers. By the end of the course students will be able to program experiments manipulate data files and conduct basic data analyses. |
| PSYC90H3 | Supervised Study in Psychology | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | An intensive research project intended to provide laboratory/field experience in data collection and analysis. The project must be completed over 2 consecutive terms.These courses provide an opportunity to engage in research in an area after completing basic coverage in regularly scheduled courses. The student must demonstrate a background adequate for the project proposed and should present a clear rationale to prospective supervisors. Regular consultation with the supervisor is necessary and extensive data collection and analysis will be required. Such a project will culminate in a written research report.Students must first find a supervisor before the start of the academic term in which the project will be initiated. They must then obtain a permission form from the Department of Psychology's website (www.utsc.utoronto.ca/psych/undergraduates) that is to be completed and signed by the intended supervisor and returned to the Psychology Office. At that time the student will be provided with an outline of the schedule and general requirements for the course including the structure of the required log-book.Students seeking supervision off campus are further advised to check the appropriateness of the proposed advisor with the Program Supervisor. If the proposed supervisor is not appointed to the Psychology faculty at UTSC then a secondary advisor that is appointed at UTSC will be required. |
| PSYC93H3 | Supervised Study in Psychology | Scarborough | Psychology (UTSC) | University of Toronto Scarborough | An intensive research project intended to provide laboratory/field experience in data collection and analysis. The project must be completed over 2 consecutive terms.These courses provide an opportunity to engage in research in an area after completing basic coverage in regularly scheduled courses. The student must demonstrate a background adequate for the project proposed and should present a clear rationale to prospective supervisors. Regular consultation with the supervisor is necessary and extensive data collection and analysis will be required. Such a project will culminate in a written research report.Students must first find a supervisor before the start of the academic term in which the project will be initiated. They must then obtain a permission form from the Department of Psychology's website that is to be completed and signed by the intended supervisor and returned to the Psychology Office. At that time the student will be provided with an outline of the schedule and general requirements for the course including the structure of the required log-book.Students seeking supervision off campus are further advised to check the appropriateness of the proposed advisor with the Program Supervisor. If the proposed supervisor is not appointed to the Psychology faculty at UTSC then a secondary advisor that is appointed at UTSC will be required. |
| SOCC31H3 | Practicum in Quantitative Research Methods | Scarborough | Sociology (UTSC) | University of Toronto Scarborough | This course provides students with hands-on experience conducting quantitative research. Each student will design and carry out a research project using secondary data. Students will select their own research questions review the relevant sociological literature develop a research design conduct statistical analyses and write up and present their findings. This course has been designated an Applied Writing Skills Course. |
| ECE448H1 | Biocomputation | St. George | Edward S. Rogers Sr. Dept. of Electrical & Computer Engin. | Faculty of Applied Science & Engineering | Modern technologies in the biosciences generate tremendous amounts of biological data ranging from genomic sequences to protein structures to gene expression. Biocomputations are the computer algorithms used to reveal the hidden patterns within this data. Course topics include basic concepts in molecular cell biology pairwise sequence alignment multiple sequence alignment fast alignment algorithms deep learning approaches phylogentic prediction structure-based computational methods gene finding and annotation. |
| MIE350H1 | Design and Analysis of Information Systems | St. George | Mechanical & Industrial Engineering | Faculty of Applied Science & Engineering | Provides students with an understanding of the mothods of information system analysis and design. These include methods for determining and documenting an organization's structure (FDD) activities behaviours and information flows (DFDs decision tables and trees network diagrams etc); model acquisition (data repositories) verification and validation. Methods such as SADT RAD and prototyping will be covered. Students will acquire a working knowledge of various frameworks for analysis (e.g. information technology categories system and application classifications decision types data vs information). Throughout the course emphasis is placed on the importance of systems thinking and organizational culture in the analysis and design process. In the laboratory students will use a CASE-based computer program (Visible Analyst) for the analysis and design of information systems for selected organizations. Students will be asked to work in teams to create a web-based information site and to document and present their development progress through the use of a structured project log. |
| MIE424H1 | Optimization in Machine Learning | St. George | Mechanical & Industrial Engineering | Faculty of Applied Science & Engineering | 1. To enable deeper understanding and more flexible use of standard machine learning methods through development of machine learning from an Optimization perspective. |
| 2. To enable students to apply these machine learning methods to problems in finance and marketing such as stock return forecasting credit risk scoring portfolio management fraud detection and customer segmentation." | |||||
| AST325H1 | Introduction to Practical Astronomy | St. George | Astronomy and Astrophysics | Faculty of Arts and Science | Through experiment and observation develop the core skills to collect reduce and interpret astronomical data. Develop understanding and usage of telescopes instruments and detectors; reduction and analysis methods; simulations and model fitting; data and error analysis. |
| AST326Y1 | Practical Astronomy | St. George | Astronomy and Astrophysics | Faculty of Arts and Science | Through experiment and observation develop the core skills to collect reduce and interpret astronomical data. Develop understanding and usage of telescopes instruments and detectors; reduction and analysis methods; simulations and model fitting; data and error analysis. This course is an expanded version of AST325H1 that gives a wider exposure to practical astronomy. |
| BCH378H1 | Biochemistry Laboratory II | St. George | Biochemistry | Faculty of Arts and Science | This course builds upon the fundamental laboratory techniques acquired in BCH377H1. Students gain hands-on experience in experimental design and data analysis exploring numerous modern and classic biochemistry and molecular biology experimental techniques used in research laboratories. Enrollment in this course is generally restricted to students enrolled in the Biochemistry Specialist program. |
| BCH441H1 | Bioinformatics | St. George | Biochemistry | Faculty of Arts and Science | This course is an introduction to computational methods and internet resources in modern biochemistry and molecular biology. The main topics include: sequence and genome databases sequence alignment and homology search use and interpretation of molecular structure and phylogenetic analysis. Assignments focus on hands-on competence building with web-based bioinformatics tools and databases downloadable software including a molecular viewer and a multiple sequence alignment editor and the statistics workbench and programming language ?R?. For syllabus details see: www.biochemistry.utoronto.ca/undergraduates/courses/BCH441H/Note BCB420H1 extends this syllabus to computational topics of systems biology. |
| CSB352H1 | Bioinformatic Methods | St. George | Cell and Systems Biology | Faculty of Arts and Science | Use of available programs for analyzing biological data. This is an introductory course with a strong emphasis on hands-on methods. Some theory is introduced but the main focus is on using extant bioinformatics tools to analyze data and generate biological hypotheses. |
| CSB471H1 | Foundational Discoveries in Genome Biology and Bioinformatics | St. George | Cell and Systems Biology | Faculty of Arts and Science | This course is based on the critical analysis of key research articles in genome biology and bioinformatics. The format is interactive and requires students to contribute actively during class meetings. Small student groups will be assigned to present context figures data methods and impact from a number of research articles during the semester. Based on the readings small student groups will propose new genome technologies or datasets and new bioinformatics software or databases. |
| CSB472H1 | Computational Genomics and Bioinformatics | St. George | Cell and Systems Biology | Faculty of Arts and Science | Computational analyses of DNA and RNA expression data. Understanding biological databases sequence alignment sequence annotation gene prediction computational analysis of function motif analysis phylogenetic analysis and gene expression profiling analysis. Applied theoretical and statistical issues will be addressed. |
| CRI350H1 | Understanding Criminological Research | St. George | Centre for Criminology and Sociolegal Studies | Faculty of Arts and Science | An introduction to social science research methods used by criminologists and to the statistical analysis of criminological data. An understanding of the strengths and weaknesses of published criminological research is developed. Specific technical issues related to sampling measurement and data analysis are taught in the context of examining ways of answering research questions. |
| IRE379H1 | Research & Analytics for Industrial Relations and Human Resources | St. George | Centre for Industrial Relations and Human Resources | Faculty of Arts and Science | Data science is changing the way organizations make decisions. This course introduces a data analytics perspective on employment relations and human resources including the measurement of performance metrics analysis of organizational policies and visualization of data. Students will develop basic data skills in the R statistical computing environment. |
| CSC258H1 | Computer Organization | St. George | Computer Science | Faculty of Arts and Science | Computer structures machine languages instruction execution addressing techniques and digital representation of data. Computer system organization memory storage devices and microprogramming. Block diagram circuit realizations of memory control and arithmetic functions. There are a number of laboratory periods in which students conduct experiments with digital logic circuits. |
| CSC317H1 | Computer Graphics | St. George | Computer Science | Faculty of Arts and Science | Identification and characterization of the objects manipulated in computer graphics the operations possible on these objects efficient algorithms to perform these operations and interfaces to transform one type of object to another. Display devices display data structures and procedures graphical input object modelling transformations illumination models primary and secondary light effects; graphics packages and systems. Students individually or in teams implement graphical algorithms or entire graphics systems. |
| CSC404H1 | Introduction to Video Game Design | St. George | Computer Science | Faculty of Arts and Science | Concepts and techniques for the design and development of electronic games. History social issues and story elements. The business of game development and game promotion. Software engineering artificial intelligence and graphics elements. Level and model design. Audio elements. Practical assignments leading to team implementation of a complete game. |
| ESS345H1 | Computational Geology | St. George | Earth Sciences | Faculty of Arts and Science | A practical introduction to programming. This course will teach an operational knowledge on how to write and execute self written computer programs. Course topics touch upon using a computer without a graphical interface using an integrated development environment programming documenting debugging reading and writing data graphical output how to navigate existing documentation and internet resources and last but not least how to effectively ask for help. Students will work individually and in small groups in an inverted classroom setting on earth science related problem sets. Previous programming experience is not required however curiosity independence and perseverance are mandatory. |
| EEB225H1 | Biostatistics for Biological Sciences | St. George | Ecology and Evolutionary Biology | Faculty of Arts and Science | A statistics course designed especially for life science students using examples from ecology and evolution where appropriate. Students learn to choose and use statistics that are appropriate to address relevant biological questions and hypotheses. Lectures and computer labs will be used to cover the following methods: sampling and experimental design data exploration correlation regression ANOVA Chi-square and non-parametric tests. |
| EEB313H1 | Quantitative Methods in R for Biology | St. George | Ecology and Evolutionary Biology | Faculty of Arts and Science | The quantitative analysis and management of biological data is crucial in modern life sciences disciplines. Students will develop skills with R as applied to problems in ecology and evolutionary biology to learn reproducible approaches for data management data manipulation visualization modelling statistical analysis and simulation for solving biological problems. |
| EEB321H1 | Community Ecology | St. George | Ecology and Evolutionary Biology | Faculty of Arts and Science | Nature and analysis of community structure; disturbance and community development; species interactions; community assembly processes. Computer exercises in weekly labs provide training in sampling simulation and data analysis. |
| ECO220Y1 | Introduction to Data Analysis and Applied Econometrics | St. George | Economics | Faculty of Arts and Science | Numerical and graphical data description; data collection and sampling; probability; sampling distributions; statistical inference; hypothesis testing and estimation; simple and multiple regression analysis (extensive coverage). Learn how to analyze data and how to correctly interpret and explain results. Use Excel to analyze a wide variety of data and replicate tables and figures in economics research papers. |
| ECO372H1 | Data Analysis and Applied Econometrics in Practice | St. George | Economics | Faculty of Arts and Science | How multiple regression can be used to answer causal questions. Implications of and how to interpret different model specifications and identification strategies. Students will read critically evaluate and replicate existing research and conduct their own original analyses. Statistical software STATA or R will be used. |
| ECO375H1 | Applied Econometrics I | St. George | Economics | Faculty of Arts and Science | Introduction to econometrics. Statistical foundations and the interpretation of multiple regression models with an emphasis on cross-sectional data. Application of regressions to a wide variety of economic questions and data sources including the use of statistical software. Problems in the identification of causality and an introduction to methods of addressing common statistical issues. |
| ECO423H1 | Economics and Biosocial Data | St. George | Economics | Faculty of Arts and Science | This course introduces and critically assesses economic research that uses genetic neuroscientific and other biosocial data. We will address questions such as: what are the effects of brain neurochemistry on economic decision-making? What role do nature and nurture play in economic behaviour and outcomes? What can we learn from genoeconomics? What are the policy implications (or lack thereof) of related findings? No previous background in biology or genetics is required. |
| ECO466H1 | Empirical Macroeconomics and Policy | St. George | Economics | Faculty of Arts and Science | This course builds on material covered in ECO208Y1 ECO325H1 and ECO374H1/ECO375H1. Students will increase their data literacy and learn to apply techniques to address policy issues. Topics covered: how monetary policy is conducted ways in which central banks use general equilibrium models and basic techniques for predicting key macroeconomic variables. Students will follow current global issues and forecast how domestic and international events may alter the Bank of Canada's monetary policy in the short run. |
| ECO475H1 | Applied Econometrics II | St. George | Economics | Faculty of Arts and Science | A research-oriented course continuing from ECO375H. The regression model is extended in several possible directions: time series analysis; panel data techniques; instrumental variables; simultaneous equations; limited dependent variables. Students will complete a major empirical term paper applying the tools of econometrics to a topic chosen by the student. |
| FRE383H1 | Quantitative Methods for the Study of French | St. George | French | Faculty of Arts and Science | An introduction to the foundations of quantitative research on French. Topics include differences between quantitative and qualitative analyses; hypothesis formulation; experimental design; and data collection and analysis including basic statistical methods. Phenomena investigated come from Canadian and European varieties as well as studies of second language learners. |
| GGR272H1 | Geographic Information and Mapping I | St. George | Geography and Planning | Faculty of Arts and Science | Introduction to digital mapping and spatial analysis using geographic information systems (GIS). Students learn how to use GIS software to find edit analyze and map geographic data to create their own maps analyze geographic problems and use techniques that can be applied to a variety of subject areas. |
| GGR273H1 | Geographic Information and Mapping II | St. George | Geography and Planning | Faculty of Arts and Science | Builds on GGR272H1 by providing students with practical spatial analysis methods and the underlying theory needed to understand how to approach various geographic problems using geographic information system (GIS) software and a variety of data types and sources. |
| LIN456H1 | Language Variation and Change: Theory and Analysis | St. George | Linguistics | Faculty of Arts and Science | The theory and practice of sociolinguistics. The inter-relationship between language and society from the perspective of collecting organizing and analyzing patterns in natural speech data including field methods and quantitative methods for correlating linguistic and social variables. |
| APM306Y1 | Mathematics and Law | St. George | Mathematics | Faculty of Arts and Science | This course examines the relationship between legal reasoning and mathematical logic; provides a mathematical perspective on the legal treatment of interest and actuarial present value; critiques ethical issues; analyzes how search engine techniques on massive databases transform legal research and considers the impact of statistical analysis and game theory on litigation strategies. NOTE This course counts as 0.5 FCE in BR3 and 0.5 FCE in BR5. This course will only contribute 0.5FCE to the Math Minor program. |
| JQR360H1 | The Canadian Census: Populations Migrations and Demographics | St. George | New College | Faculty of Arts and Science | Examines the Canadian population census through the experience of diasporic groups in Canada. Approaches the census as a statistical tool an historical source and an ideological project of citizenship and nationalism. Uses census data to explore mathematical and statistical concepts and to integrate numerical ways of thinking with qualitative analysis. (Jointly sponsored by African Studies Diaspora and Transnational Studies Caribbean Studies Equity Studies and Latin American Studies). |
| PHY224H1 | Practical Physics I | St. George | Physics | Faculty of Arts and Science | Develops the core practical experimental and computational skills necessary to do physics. Students tackle simple physics questions involving mathematical models computational simulations and solutions experimental measurements data and uncertainty analysis. |
| PHY324H1 | Practical Physics II | St. George | Physics | Faculty of Arts and Science | A modular practical course that further develops the core experimental and computational skills necessary to do physics. Modules include: experimental skills building computational tools in data and uncertainty analysis and independent experimental projects. |
| PSY305H1 | The Treatment of Psychological Data | St. George | Psychology | Faculty of Arts and Science | This course provides a practical yet intensive introduction to the research pipeline with a focus on research data management and advanced statistical analysis and inference. Students learn how to find organize and analyze data sets in a transparent and reproducible way. Students also learn more about statistical inference focusing on how the design and analysis of data shape the interpretation of results. |
| PSY371H1 | Higher Cognitive Processes | St. George | Psychology | Faculty of Arts and Science | This course covers selected topics pertaining to higher cognitive processes including expertise consciousness creativity and human and artificial intelligence. |
| MGT201H1 | Introduction to Financial Accounting | St. George | Rotman Commerce | Faculty of Arts and Science | Introduction to financial reporting and analysis that is used by companies to organize and evaluate data in light of their organization?s goal. Emphasis is on decision-making and interpretation of financial statements and how they can be used to plan a firm?s overall business activities through the use of real-world companies. Not open to Rotman Commerce students. Not eligible for CR/NCR option. Contact Rotman Commerce for details. |
| RSM326H1 | Data Analytics with Financial Accounting Information | St. George | Rotman Commerce | Faculty of Arts and Science | Students will learn how to better understand and analyze accounting information through empirical analysis. The course will teach students how to extract information from rich accounting and finance datasets to help provide insights in a wide range of corporate business problems in both equity and debt markets. Different modeling approaches are used to analyze accounting data and disclosure information. Not eligible for CR/NCR. |
| RSM456H1 | Big Data and Marketing Analytics | St. George | Rotman Commerce | Faculty of Arts and Science | The course is designed to introduce students to tools used in marketing analytics. Companies have been collecting vast databases to aid them in making sound marketing decisions. Examples include retail scanner panel data which keeps track of customers? purchase histories loyalty-program data monitoring purchasing under different promotional environments social network and online shopping history data. The course uses several marketing data sources to illustrate how to use statistical marketing models to evaluate the impacts of marketing-mix and manage customer lifetime value. Not eligible for CR/NCR option. Contact Rotman Commerce for details. |
| SOC252H1 | Intermediate Quantitative Methods in Sociology | St. George | Sociology | Faculty of Arts and Science | Provides students with the opportunity to develop an understanding of the logic of multivariate analysis by applying various strategies for the analysis of complex multivariate data. Restricted to sociology majors and specialists. |
| SOC320H1 | Family Demography | St. George | Sociology | Faculty of Arts and Science | Uses empirical sociological studies to describe and analyze the political social and economic implications of diverse family relationships and living arrangements. Examines the social and economic consequences of inconsistencies between public definitions of family and the realities of family life. Introduces students to the statistical analysis of the demographic features of families using census data. This is a program-only course and is restricted to sociology majors and specialists. |
| STA248H1 | Statistics for Computer Scientists | St. George | Statistical Sciences | Faculty of Arts and Science | A survey of statistical methodology with emphasis on data analysis and applications. The topics covered include descriptive statistics data collection and the design of experiments univariate and multivariate design tests of significance and confidence intervals power multiple regression and the analysis of variance and count data. Students learn to use a statistical computer package as part of the course (Note: STA248H1 does not count as a distribution requirement course). |
| STA288H1 | Statistics and Scientific Inquiry in the Life Sciences | St. George | Statistical Sciences | Faculty of Arts and Science | Introduction to statistics and its connection to all stages of the scientific inquiry process. Issues around data collection analysis and interpretation are emphasized to inform study design and critical assessment of published research. Statistical software is used to conduct descriptive and inferential statistics to address basic life sciences research questions. |
| STA465H1 | Theory and Methods for Complex Spatial Data | St. George | Statistical Sciences | Faculty of Arts and Science | Data acquisition trends in the environmental physical and health sciences are increasingly spatial in character and novel in the sense that modern sophisticated methods are required for analysis. This course will cover different types of random spatial processes and how to incorporate them into mixed effects models for Normal and non-Normal data. Students will be trained in a variety of advanced techniques for analyzing complex spatial data and upon completion will be able to undertake a variety of analyses on spatially dependent data understand which methods are appropriate for various research questions and interpret and convey results in the light of the original questions posed. |
| COG403H1 | Seminar in Cognitive Science | St. George | University College | Faculty of Arts and Science | Advanced treatment of cognitive science topics including the application of core ideas from probability theory information theory statistics and machine learning to modelling human cognition and artificial intelligence. |
| VIC171Y1 | Methodology Theory and Practice in the Natural Sciences | St. George | Victoria College | Faculty of Arts and Science | An examination of scientific theories and their logic in life and physical sciences. Experimental design novel device production data analysis and modeling will be discussed using examples drawn from primary source material in the natural sciences. Students will prepare a research paper on a topic designed in consultation with the instructor. Restricted to first-year students. Not eligible for CR/NCR option. |
Introductory Courses
| Code | Course Name | Campus | Department | School | Description |
|---|---|---|---|---|---|
| JCP265H5 | Introduction to Scientific Computing | Mississauga | Chemical and Physical Sciences | University of Toronto Mississauga | This course is an introduction to computing in the physical sciences. Students will gain experience utilizing numerical software tools used in both academic and industrial settings. A variety of numerical techniques will be covered with topics to include: curve fitting numerical approximations of derivatives and integrals root finding solutions of differential equations Fourier series Monte Carlo methods and more. Students will also acquire skills in data analysis and visualization. No prior experience in computer programming is required. [24L 24P] |
| CCT111H5 | Critical Coding | Mississauga | Institute of Communication and Culture | University of Toronto Mississauga | This experiential learning course introduces students to the practice and theory of coding programming and basic development of user-oriented software. The lectures illustrate a core range of software development concepts that provide the foundations needed for the practical coding of front-end applications such as mobile interfaces or of back-end software such as introductory artificial intelligence or social media analysis. The practicals are lab-based and focus on applying these theoretical skills to solving problems grounded in a critical understanding of the interaction between people culture and society by developing software or apps in languages such as Java Objective C Swift Python. [24L 12P] |
| LIN318H5 | Talking Numbers: Interpretation and Presentation of Quantitative Linguistic Data | Mississauga | Language Studies | University of Toronto Mississauga | Do numbers and statistics make your vision go blurry? Do you avoid making eye contact with charts and tables? From measuring vowel formants to gradient grammaticality judgments to frequencies and patterns in natural language corpora research in linguistics is becoming increasingly dependent on quantitative data and argumentation... but fear not! In this course students with no prior background in statistics will learn the fundamentals of quantitative reasoning through hands-on experience with contemporary statistical tools and will be equipped with the basic numeracy skills necessary to critically evaluate quantitative arguments in a range of subfields of linguistics. Formerly LIN368H5. [24L 12T] |
| CSCA08H3 | Introduction to Computer Science I | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | Programming in an object-oriented language such as Python. Program structure: elementary data types statements control flow functions classes objects methods. Lists; searching sorting and complexity. This course is intended for students having a serious interest in higher level computer science courses or planning to complete a computer science program. |
| CSCA20H3 | Introduction to Programming | Scarborough | Dept. of Computer & Mathematical Sci (UTSC) | University of Toronto Scarborough | An introduction to computer programming with an emphasis on gaining practical skills. Introduction to programming software tools database manipulation. This course is appropriate for students with an interest in programming and computers who do not plan to pursue a Computer Science program. |
| PSCB57H3 | Introduction to Scientific Computing | Scarborough | Dept. of Physical & Environmental Sci (UTSC) | University of Toronto Scarborough | Scientific computing is a rapidly growing field because computers can solve previously intractable problems and simulate natural processes governed by equations that do not have analytic solutions. During the first part of this course students will learn numerical algorithms for various standard tasks such as root finding integration data fitting interpolation and visualization. In the second part students will learn how to model real-world systems from various branches of science. At the end of the course students will be expected to write small programs by themselves. Assignments will regularly include programming exercises. |
| SOCB35H3 | Numeracy and Society | Scarborough | Sociology (UTSC) | University of Toronto Scarborough | This course introduces the basic concepts and assumptions of quantitative reasoning with a focus on using modern data science techniques and real-world data to answer key questions in sociology. It examines how numbers counting and statistics produce expertise authority and the social categories through which we define social reality. This course avoids advanced mathematical concepts and proofs. |
| CSC108H1 | Introduction to Computer Programming | St. George | Computer Science | Faculty of Arts and Science | Programming in a language such as Python. Elementary data types lists maps. Program structure: control flow functions classes objects methods. Algorithms and problem solving. Searching sorting and complexity. Unit testing. No prior programming experience required.NOTE: You may not take this course concurrently with CSC120H1/CSC148H1 but you may take CSC148H1 after CSC108H1. |
| CSC110Y1 | Foundations of Computer Science I | St. George | Computer Science | Faculty of Arts and Science | An introduction to the field of computer science combining the tools and techniques of programming (using the Python programming language) with rigorous mathematical analysis and reasoning. Topics include: data representations; program control flow (conditionals loops exceptions functions); mathematical logic and formal proof; algorithms and running time analysis; software engineering principles (formal specification and design testing and verification). Prior programming experience is not required to succeed in this course. This course is restricted to students in the first year Computer Science admission stream and is only offered in the Fall term. Other students planning to pursue studies in computer science should enrol in CSC108H1 CSC148H1 and CSC165H1/CSC240H1. |
| CSC111H1 | Foundations of Computer Science II | St. George | Computer Science | Faculty of Arts and Science | A continuation of CSC110Y1 to extend principles of programming and mathematical analysis to further topics in computer science. Topics include: object-oriented programming (design principles encapsulation composition and inheritance); binary representation of numbers; recursion and mathematical induction; abstract data types and data structures (stacks queues linked lists trees graphs); the limitations of computation. This course is restricted to students in the first year Computer Science admission stream and is only offered in the Winter term. Other students planning to pursue studies in computer science should enrol in CSC108H1 CSC148H1 and CSC165H1/CSC240H1. |
| CSC148H1 | Introduction to Computer Science | St. George | Computer Science | Faculty of Arts and Science | Abstract data types and data structures for implementing them. Linked data structures. Encapsulation and information-hiding. Object-oriented programming. Specifications. Analyzing the efficiency of programs. Recursion. This course assumes programming experience as provided by CSC108H1. Students who already have this background may consult the Computer Science Undergraduate Office for advice about skipping CSC108H1. Practical (P) sections consist of supervised work in the computing laboratory. These sections are offered when facilities are available and attendance is required. NOTE: Students may go to their college to drop down from CSC148H1 to CSC108H1. See above for the drop down deadline. |
| JCC250H1 | Computing for Science | St. George | Computer Science | Faculty of Arts and Science | Computational skills for the modern practice of basic and applied science. Applied computer programming with an emphasis on practical examples related to the simulation of matter drawing from scientific disciplines including chemistry biology materials science and physics. Studio format with a mixture of lecture guided programming and open scientific problem solving. Students will be exposed to Python numerical and data analysis libraries. No prior programming experience is required. |
Talking About A/AI
| Code | Course Name | Campus | Department | School | Description |
|---|---|---|---|---|---|
| CCT431H5 | Drones Robots Artificial Intelligence | Mississauga | Institute of Communication and Culture | University of Toronto Mississauga | Drones robots and artificial intelligence are three interrelated technologies that are changing the most fundamental considerations of how society and sociality should operate. Work war consumption and even love are being reconfigured. This course will address debates concerning the cultural political economic military and economic considerations surrounding the growing use of these technologies. [24L] |
| WRI363H5 | Communicating in a World of Data | Mississauga | Institute of Communication and Culture | University of Toronto Mississauga | This course examines theory and offers practice in analyzing interpreting and communicating data in an understandable and engaging manner. The course explores the growing relevance and allure of Data in all its forms. Students will learn to interpret data to tell a story through numbers by creating infographics writing informative articles from their own data mining and presenting further findings at the end of the semester. The course draws on a range of theorists and data experts including Arvind Sathi Kenneth Cukier Viktor Mayer-Schonberger and Eric Siegel. [24L] |
| EDS285H5 | The Future of Ed Tech: Active Learning Classrooms and Artificial Intelligence | Mississauga | Language Studies | University of Toronto Mississauga | This course will explore research on emerging digital models platforms apps and policies that seek to further customize enhance and bring greater equity to education through technology. From the initiation of open courseware to the inception of virtual reality artificial intelligence ALC classrooms makerspaces and the Òshared economyÓ this course will foster a culture of digital innovation to investigate accelerate test and study new possibilities and advancements in the field of educational technology. [24L] |
| PSY371H5 | Higher Cognitive Processes | Mississauga | Psychology | University of Toronto Mississauga | This course covers selected topics pertaining to higher cognitive processes including expertise consciousness creativity and human and artificial intelligence. [36L] |
| JOUC20H3 | Emerging Tools and Technology | Scarborough | Dept. of Arts Culture & Media (UTSC) | University of Toronto Scarborough | From drones to virtual reality and from augmented reality to artificial intelligence this course will open students’ minds to innovation in storytelling and communications and provide opportunities to discover and explore through interaction with leading-edge practitioners in communications and journalism. This course is taught at Centennial College and is open only to students in the Specialist (Joint) program in Journalism. |
| CSC199H1 | Intelligence Artificial and Human | St. George | Computer Science | Faculty of Arts and Science | What is human intelligence? How close are we to replicating it? How productive/reductive is the brain-computer analogy? What ethical challenges are posed by AI on workers society and the environment? Can we put a hold on "progress"? Is Silicon Valley the seat of a new techno-religion? What can they teach us about today's research priorities? What insight (or inspiration) can we get from works of science fiction about the future of human-AI interaction? Through reading discussion written assignment and workshops this seminar will present students with the opportunity to integrate their computer science interests with philosophy history and literature. There is an equivalent course offered by St. Michael?s College. Students may take one or the other but not both. Restricted to first-year students. Not eligible for CR/NCR option. |
| CSC300H1 | Computers and Society | St. George | Computer Science | Faculty of Arts and Science | This course offers a concise introduction to ethics in computing distilled from the ethical and social discussions carried on by today's academic and popular commentators. This course covers a wide range of topics within this area including the philosophical framework for analyzing computer ethics; the impact of computer technology on security privacy and intellectual property digital divide and gender and racial discrimination; the ethical tensions with Artificial Intelligence around future of work and humanity the emerging role of online social media over voice inclusion and democracy; and the environmental consequences of computing. |
| HIS393H1 | Digital History | St. George | History | Faculty of Arts and Science | Explores implications for history and its methods of the shift from print to digital sources. Imparts introductory skills in the manipulation digital media such as the use of maps GIS and big data. |
| HPS255H1 | History and Philosophy of Artificial Intelligence | St. George | Inst. for the History & Philosophy of Science & Technology | Faculty of Arts and Science | This course introduces students to the historical and philosophical issues around artificial intelligence (AI). We will cover the geopolitical economic and cultural contexts from which the field of AI emerged as well as the troubled history of the scientific concept of intelligence and how that has influenced the development of AI. The course will also introduce students to foundational and normative questions such as how we should define and measure AI how to evaluate the accomplishments of AI systems and what the benefits and risks of relying on such systems might be. |
| HPS345H1 | Quantifying the World: the Debates on the Ethical and Epistemic Implications of AI and Automation | St. George | Inst. for the History & Philosophy of Science & Technology | Faculty of Arts and Science | The effects of automation computing and information technology have had a great impact on our society. The rise of automation and computing the almost cult-like trust in mechanization have transformed our society both at the material and the epistemological level. This course will examine the epistemological and ethical debates that AI and automation have produced in all sectors of society. It will consider a variety of media and instruments from data visualization and mapping to the use of AI and robotics contextualizing them within popular and hotly contested examples in the military field and in cybersecurity in medical diagnostics and epidemiology in the automotive industry and in the personal realm. |
| HPS346H1 | Modifying and Optimizing Life: on the Peculiar Alliance between AI Biology and Engineering | St. George | Inst. for the History & Philosophy of Science & Technology | Faculty of Arts and Science | Taking cue from the entanglements that historically have pervaded the relation between biology and information technology since the early 20th century this course interrogates the sociocultural and technological conjuncture that has brought computer science biology and engineering together into peculiar ingenious and often controversial alliances. What do AI synthetic biology and biotechnology have in common? How have they come to be associated? What are the debates and ethics emerging from such associations? The course will focus on topics such as: geoengineering and bioremediation; GMO and Robotic insects; the use of expert systems and machine learning to optimize synthetic biology; the flourishing and marketing of precision and personalized medicine/immunotherapy; and the ethics behind CRISPR babies. |
| PHL256H1 | Philosophy in the Age of the Internet | St. George | Philosophy | Faculty of Arts and Science | The internet and digital technology have had a transformative impact on the economy society and politics art and culture and everyday life. This course explores the fascinating often urgent new philosophical questions raised by these changes as well as the way they invite a rethinking of many older philosophical questions. Topics to be addressed may include artificial intelligence and the singularity; identity through social media; digital ownership and privacy; and collective/distributed knowledge its relation to information among others. |
| PHL342H1 | Minds and Machines | St. George | Philosophy | Faculty of Arts and Science | Topics include: philosophical foundations of artificial intelligence theory; the computational theory of the mind; functionalism vs. reductionism; the problems of meaning in the philosophy of mind. |
| JPH441H1 | Physical Science in Contemporary Society | St. George | Physics | Faculty of Arts and Science | Complex nature of the scientific method; connection between theory concepts and experimental data; insufficiency of reductionism; characteristics of pathological and pseudo-science; public perception and misperception of science; science and public policy; ethical issues; trends in modern science. |
| SMC199H1 | Intelligence Artificial and Human | St. George | St. Michael's College | Faculty of Arts and Science | What is human intelligence? How close are we to replicating it? How productive/reductive is the brain-computer analogy? What ethical challenges are posed by AI on workers society and the environment? Can we put a hold on "progress"? Is Silicon Valley the seat of a new techno-religion? What can they teach us about today's research priorities? What insight (or inspiration) can we get from works of science fiction about the future of human-AI interaction? Through reading discussion written assignment and workshops this seminar will present students with the opportunity to integrate their computer science interests with philosophy history and literature. There is an equivalent course offered by the Department of Computer Science. Students may take one or the other but not both. Restricted to first-year students. Not eligible for CR/NCR option. |
| CSC104H1 | Computational Thinking | St. George | Computer Science | Faculty of Arts and Science | Humans have solved problems for millennia on computing devices by representing data as diverse numbers text images sound and genomes and then transforming the data. A gentle introduction to designing programs (recipes) for systematically solving problems that crop up in diverse domains such as science literature and graphics. Social and intellectual issues raised by computing. Algorithms hardware software operating systems the limits of computation. Note: you may not take this course concurrently with any Computer Science course but you may take CSC108H1/CSC148H1 after CSC104H1. |
Undergraduate courses
AER336H1: Scientific Computing
AER525H1: Robotics
APS105H1: Computer Fundamentals
APS106H1: Fundamentals of Computer Programming
APS360H1: Artificial Intelligence Fundamentals
BME445H1: Neural Bioelectricity
CME261H1: Engineering Mathematics I
CME262H1: Engineering Mathematics II
CSC263H1: Data Structures and Analysis
CSC311H1: Introduction to Machine Learning (formerly CSC411H1: Machine Learning and Data Mining)
CSC321H1: Introduction to Neural Networks and Machine Learning
CSC373H1: Algorithm Design, Analysis & Complexity
CSC384H1: Introduction to Artificial Intelligence
CSC412H1: Probabilistic Learning and Reasoning
CSC421H1: Neural Networks and Deep Learning
CSC443H1: Database System Technology
CSC485H1: Computational Linguistics
CSC486H1: Knowledge Representation and Reasoning
ECE302H1: Probability and Applications
ECE324H1: Introduction to Machine Intelligence
ECE345H1: Algorithms and Data Structures
ECE358H1: Foundations of Computing
ECE367H1: Matrix Algebra and Optimization
ECE368H1: Probabilistic Reasoning
ECE421H1: Introduction to Machine Learning
ECE521: Inference Algorithms and Machine Learning
ESC384H1: Partial Differential Equations
MAT290H1: Advanced Engineering Mathematics
MAT389H1: Complex Analysis
MIE253H1: Data Modelling
MIE335H1: Algorithms & Numerical Methods
MIE368H1: Analytics in Action
MIE424H1: Optimization in Machine Learning
MSE238H1: Engineering Statistics and Numerical Methods
PHL342H1: Minds and Machines
ROB311H1: Artificial Intelligence
ROB313H1: Introduction to Learning from Data
STA314H1: Statistical Methods for Machine Learning I
Graduate courses
APS502H: Financial Engineering
APS1005H: Operations Research for Engineering Management
APS1017H: Supply Chain Management and Logistics
APS1022H: Financial Engineering II
APS1040H: Quality Control for Engineering Management
APS1050H: Blockchain Technologies and Cryptocurrencies
APS1051H: Portfolio Management Praxis Under Real Market Constraints
APS1052H: A.I. in Finance
APS1070H: Foundations of Data Analytics and Machine Learning
CEM1002H: Data Analytics and Cities
CHE507H: Data-based Modelling for Prediction and Control
CHE1147H: Data Mining in Engineering
CHE1148H: Process Data Analytics
CHE1434H: Six Sigma for Chemical Processes
CIV1504H: Applied Probability and Statistics for Civil Engineering
CIV1506H: Freight Transportation and ITS Applications
CIV1507H: Public Transport
CIV1532H: Fundamentals of ITS and Traffic Management
CIV1538H: Transportation Demand Analysis
ECE537H: Random Processes
ECE1504H: Statistical Learning
ECE1505H: Convex Optimization
ECE1510H: Advanced Inference Algorithms
ECE1513H: Introduction to Machine Learning
ECE1657H: Game Theory and Evolutionary Games
ECE1762H: Algorithms and Data Structures
ECE1778H: Creative Applications for Mobile Devices
ECE1779H: Introduction to Cloud Computing
ECE1784H: Trustworthy Machine Learning
MIE562H: Scheduling
MIE1413H: Statistical Models in Empirical Research
MIE1501H: Knowledge Modelling and Management
MIE1512H: Data Analytics
MIE1513H: Decision Support Systems
MIE1620H: Linear Programming and Network Flows
MIE1621H: Non-Linear Optimization
MIE1622H: Computational Finance and Risk Management
MIE1623H: Introduction to Healthcare Engineering
MIE1624H: Introduction to Data Science and Analytics
MIE1624H: Introduction to Data Science and Analytics
MIE1628H: Big Data Science
MIE1653H: Integer Programming Applications
MIE1721H: Reliability
MIE1723H: Engineering Asset Management
MIE1727H: Statistical Methods of Quality Assurance
