Public repository for lecture notes / labs, etc
- Semester: Spring 2025
- Instructor: Tao LIN and Kaicheng YU
- Time and Location:
- Theory: Tuesday 09:50 - 12:15, YunGu campus E10-212
- Exercise: Thursday 08:00 - 09:35, YunGu campus E10-221
See course_info_sheet for more details.
| Week | Session | Class Hour | Instructor | Theme / Topic | Teaching Activities (Lecture/Practical) |
|---|---|---|---|---|---|
| 1st | Foundations of Data Science | Tao Lin | Course logistics; Introduction to ML (why ML, and why now); Linear algebra review; Probability review | Lecture | |
| Foundations of Data Science | TA | Lab 1 (graded): mathematical foundations | Practical | ||
| 2nd | Linear Models for Regression | Tao Lin | Linear regression; Cost functions; Introduction to optimization | Lecture | |
| Linear Models for Regression | TA | Lab 2: Introduction to Python, NumPy, and PyTorch; Lab 1 due; Project 1 release | Practical | ||
| 3rd | Linear Models for Regression | Tao Lin | Least squares; Probabilistic interpretation: Maximum Likelihood Estimation (MLE); Over- and under-fitting; Ridge regression; Lasso | Lecture | |
| Linear Models for Regression | TA | Lab 3 | Practical | ||
| 4th | Generalization, and Model Selection | Tao Lin | Generalization; Bias-Variance decomposition; Double descent phenomenon; Model selection, and validation | Lecture | |
| Generalization, and Model Selection | TA | Lab 4 (graded) | Practical | ||
| 5th | Linear Models for Classification | Tao Lin | Classification; Logistic regression; Logistic regression and its optimization (MLE, Steepest descent, Newton's method, etc.) | Lecture | |
| Linear Models for Classification | TA | Lab 5; Lab 4 due | Practical | ||
| 6th | Generalized Linear Models | Tao Lin | Exponential family; Generalized linear models | Lecture | |
| Generalized Linear Models | TA | QA for Project 1; | Practical | ||
| 7th | Generative Learning Algorithms | Tao Lin | Discriminative vs. Generative learning algorithms; Gaussian Discriminant Analysis (GDA); GDA and Linear Discriminant Analysis (LDA); GDA and Naïve Bayes; GDA vs. Logistic regression | Lecture | |
| Generative Learning Algorithms | TA | QA for Project 1; Project 1 due; Project 2 release | Practical | ||
| 8th | Kernel Methods, SVM | Tao Lin | Kernel methods; Support Vector Machine (SVM) | Lecture | |
| Kernel Methods, SVM | TA | Lab 6 (graded); | Practical | ||
| 9th | Nonparametric Methods | Kaicheng Yu | Parametric vs. nonparametric models; K-nearest neighbors; Decision trees; Bagging and random forest | Lecture | |
| Nonparametric Methods | TA | Lab 7; Lab 6 due | Practical | ||
| 10th | Mixture Models, EM Algorithm | Kaicheng Yu | Introduction to unsupervised Learning; Clustering; K-means; Gaussian Mixture Model (GMM); EM algorithm | Lecture | |
| Mixture Models, EM Algorithm | TA | Lab 8 | Practical | ||
| 11th | EM Algorithm, Dimensionality Reduction | Kaicheng Yu | EM algorithm; Alternative view of EM: GMM revisited, relation to K-means | Lecture | |
| EM Algorithm, Dimensionality Reduction | TA | Lab 9 (graded) | Practical | ||
| 12th | Neural Networks | Kaicheng Yu | Neural Networks - basics, representation power; Neural Networks – backpropagation | Lecture | |
| Neural Networks | TA | Lab 10; Lab 9 due | Practical | ||
| 13th | Deep Neural Networks | Kaicheng Yu | Deep Neural Networks – advanced architectures (CNN, RNN, Transformer, etc.) | Lecture | |
| Deep Neural Networks | TA | Lab 11 (graded) | Practical | ||
| 14th | Deep Neural Networks | Kaicheng Yu | Deep Neural Networks – optimization | Lecture | |
| Deep Neural Networks | TA | Lab 12; Lab 11 due; QA for Project 2 | Practical | ||
| 15th | Deep Neural Networks | Kaicheng Yu | Self-supervised Learning; LLMs | Lecture | |
| TA | Lab 13; QA for Project 2 | Practical | |||
| 16th | Recitation | Tao Lin, Kaicheng Yu | Course recitation | Lecture | |
| TA | QA for Project 2; Project 2 due | Practical |