This course introduces techniques for computational data analysis, with an emphasis on machine learning techniques, which extracts useful knowledge from data in real-world applications. On the technique side, we will cover key machine learning methods (supervised learning, representation learning, generative models, and foundation models). On the application side, it will introduce various applications of these techniques, including images/text generation and robotics. It will introduce how to formulate real-world tasks as data analysis problems, key methods for solving these problems, and their advantages and disadvantages. 

These topics will be covered in Four Modules:

• Module I: Background Knowledge
  • Linear Algebra
  • Probability and Statistics
  • Optimization
• Module II: Supervised Learning
  • Linear Regression and Classification
    • Ridge Regression, Logistic Regression, Naive Bayes
  • Neural Networks
    • CNN, RNN
• Module III: Unsupervised Learning
  • Clustering
    • K-means, Gaussian Mixture Models
  • Dimension Reduction and Representation Learning
    • PCA, SimCLR
  • Generative Models
    • VAE
• Module IV: Large Language Models (LLM)
  • Attention, Transformer
  • Supervised Fine-Tuning
  • Reinforcement Learning with Human Feedback (RLHF)