COMP5212
, , 1900
| Slides | Date | Topic | Readings | Assignments |
|---|---|---|---|---|
| Lecture 0 | 31/01 Wed | Introduction | ||
| Lecture 1 | 02/02 Fri | Math basics | ||
| Lecture 2 | 07/02 Wed | Supervised learning basics | ||
| Lecture 3 | 09/02 Fri | Logistic regression | ||
| Lecture 4 | 14/02 Wed | Generalized linear models, classification | ||
| Lecture 5 | 16/02 Fri | Kernel methods | ||
| Lecture 6 | 21/02 Wed | SVM | ||
| Lecture 7 | 23/02 Fri | Naive Bayes | ||
| Lecture 8 | 28/02 Wed | MLE, MAP | ||
| Lecture 9 | 01/03 Fri | Gradient descent, SGD, Newton’s method | ||
| Lecture 10 | 06/03 Wed | Generalization, bias-variance tradeoff | ||
| Lecture 11 | 08/03 Fri | Clustering | ||
| Lecture 12 | 13/03 Wed | Expectation Maximization | ||
| Lecture 13 | 15/03 Fri | PCA/ICA | ||
| Lecture 14 | 20/03 Wed | mid-term exam | ||
| Lecture 15 | 22/03 Fri | Probabilistic Graphical Models | ||
| Lecture 16 | 27/03 Wed | HMM | ||
| 29/03 Fri | Good Friday holiday | |||
| 03/04 Wed | mid-term break | |||
| 05/04 Fri | mid-term break | |||
| Lecture 17 | 10/04 Wed | Neural Networks, backprop | ||
| Lecture 18 | 12/04 Fri | Neural Networks, architectures | ||
| Lecture 19 | 17/04 Wed | Neural architectures | ||
| Lecture 20 | 19/04 Fri | Variational autoencoder | ||
| Lecture 21 | 24/04 Wed | Generative adversarial networks | ||
| Lecture 22 | 26/04 Fri | Reinforcement Learning | ||
| 01/05 Wed | Labor day | |||
| Lecture 23 | 03/05 Fri | Languge models | ||
| Lecture 24 | 08/05 Wed | Pretraining | ||
| Lecture 25 | 10/05 Fri | Large language models |