AI Training

Day 1: Machine Learning Basics

• Morning:
  • Lecture: Introduction to Machine Learning, including supervised and unsupervised learning.
  • Workshop: Hands-on practice with basic algorithms, such as regression and classification.
• Afternoon:
  • Lecture: Model evaluation techniques and validation strategies.
  • Workshop: Practical exercises on model tuning and evaluation.

Day 2: Application of ML Technologies to Bioscience

• Morning:
  • Lecture: Review of existing machine learning algorithms, including SVMs, decision trees, and ensemble methods, and their general applications.
  • Workshop: Hands-on coding session with these algorithms, followed by a discussion on how they can be applied to bioscience problems.
• Afternoon:
  • Lecture: Introduction to unsupervised learning methods like clustering and dimensionality reduction, with an emphasis on their relevance to gene expression analysis or other biological data.
  • Workshop: Applying unsupervised learning techniques to a public bioscience dataset.

Day 3: Deep Learning

• Morning:
  • Lecture: Introduction to deep learning and neural networks.
  • Workshop: Hands-on experience with building simple deep learning models.
• Afternoon:
  • Lecture: Overview of cutting-edge models like GPT and their applications.
  • Workshop: Experimentation with pre-trained GPT models and customization for specific tasks.

Day 1 (Optional)

• Lecture 1: Basics of NNs
• Workshop 1: Implementing a Simple NN
• Lecture 2: Regularization and Hyperparameter Tuning
• Workshop 2: Fine-tuning a NN

Day 2

• Lecture 1: CNNs
• Workshop 1: Building a CNN for image classification
• Lecture 2: RNNs and LSTMs
• Workshop 2: Implementing an RNN for text generation

Day 3

• Lecture 1: Advanced Deep Learning
• Workshop 1: Implementing advanced techniques
• Lecture 2: NLP and LLMs
• Workshop 2: Using LLMs for NLP tasks