This schedule is tentative and may change.
| Date | Topic | Materials |
|---|---|---|
| January 16 | Course Introduction |
|
| January 18 | Supervised Learning | |
| January 23 | Logistic Regression | |
| January 25 | Regularization and Probability Theory | |
| January 30 | Linear Discriminant Analysis | |
| February 1 | Naive Bayes Classifier, Kernels | |
| February 6 | Support Vector Machine | |
| February 8 | Support Vector Machine | |
| February 13 | Perceptrons | |
| February 15 | Artificial Neural Networks | |
| February 20 | ANNs, Bias-Variance Tradeoff, Model Selection, and Cross-Validation | |
| February 22 | Decision Trees | |
| February 27 | Random Forests and Boosting | |
| February 29 | CANCELLED |
|
| March 5 | Gradient Boosting | |
| March 7 | Intro. to Deep Learning | |
| March 19 | Convolutional Neural Networks | |
| March 21 | Optimization for Deep Learning | |
| March 26 | Recurrent Neural Networks | |
| March 28 | Transformers | |
| April 2 | Clustering |
|
| April 4 | Principal Component Analysis | |
| April 9 | Markov Decision Processes | |
| April 11 | Reinforcement Learning | |
| April 16 | CANCELLED |
|
| April 18 | Policy Gradient Methods, NLP | |
| April 23 | Large Language Models | |
| April 25 | Project Presentations |
|
| April 30 | Project Presentations |
|