DDA5001 Machine Learning

This is the public version of DDA5001 Machine Leanring for the 2025 Fall semester conducted at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. For students who are taking this course at CUHK-Shenzhen, the official website is on Blackboard.

Disclaimer: Part of this course’s contents are from the following book:

  • Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin. Learning from data. Vol. 4. New York: AMLBook, 2012.

We also refer to the contents from books such as:

  • Christopher M. Bishop, Hugh Bishop. Deep learning: Foundations and concepts. Springer Nature, 2023.
  • Kevin P. Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.

All the slides are developed by us from scratch, while sometimes using figures from books and internet.

Lecture Contents

The lecture contents are listed by weeks and each week has two lectures.

  1. The course introduction. Basic math (especially linear algebra). Concepts of learning.
      [slides1]
      [slides2]

  2. Linear classification: Perceptron. Linear regression: Least squares and maximum likelihood estimation.
      [slides3]
      [slides4]

  3. Training versus testing.
      [slides5] and its supplementary material [Proofs of Porbabilistic Inequalities]
      [slides6]

  4. Training versus testing. Logistic regression.
      [slides7] and its supplementary material [Proofs for VC Dimension]
      [slides8]

  5. Gradient-based optimization algorithms: GD and momentum.
      [slides9] and its supplementary material [Proof for GD Convergence]
      [slides10]

  6. Overfitting.
      [slides11]
      [slides12]
      [slides13]

  7. SVM and Kernel Methods.
      [slides14]
      [slides15]

  8. Unsupervised learning: PCA and k-means.
      [slides16]
      [slides17]

  9. Neural networks: Model, BP, and Adam.
      [slides18]
      [slides19]
      [slides20]

  10. Introductory deep learning.
      [slides21]

  11. Attention, transformer, and LLMs
      [slides22]
      [slides23]
      [slides24]
      [slides25]

Final Projects

  [project manual]
  [project repo]