Course Schedule
The following tentative course schedule will be followed. The schedule is flexible and might change due to progression paste, students' feedback, and unforeseen issues.
| Event | Week | Description | Required reading |
|---|---|---|---|
| Assignment(p0) | 0 | Warmup: Python, NumPy, Pandas | |
| Lecture | 1 | Introduction | Linear Algebra, Probability, Python |
| Lecture | 1 | K-nearest neighbors | KNN |
| Assignment(w1) | 2 | K-nearest neighbors | |
| Lecture | 2 | Perceptron | NYT |
| Assignment(p1) | 2 | Perceptron | |
| Lecture | 2 | Inference from probabilities | |
| Lecture | 3 | Bayesian networks | |
| Lecture | 3 | Naive Bayes | Tom Mitchell's book Chp 3, Sec 1,2 |
| Lecture | 4 | Logistic Regression | Tom Mitchell's book Chp 3, Sec 3 |
| Lecture | 4 | Linear Regression | |
| Lecture | 5 | Gradient Descent | GD Overview |
| Assignment(p2) | 5 | Logistic and Linear Regression | |
| Lecture | 5 | Linear SVM | |
| Assignment(p3) | 6 | SVM | |
| Lecture | 6 | Empirical Risk Minimization | |
| Assignment(w2) | 6 | Empirical Risk Minimization | |
| Lecture | 6 | ML Debugging, Over-Underfitting | Andrew Ng's notes |
| Lecture | 7 | Bias/Variance Tradeoff | |
| Lecture | 7 | Kernels | |
| Lecture | 8 | Decision/Regression Trees | |
| Lecture | 8 | Bagging | |
| Lecture | 9 | Boosting | Boosting Paper |
| Assignment(p4) | 9 | Decision/Regression Trees | |
| Lecture | 9 | Artificial Neural Networks/Deep Learning | Deep Learning Chp 2 |
| Lecture | 10 | Python Automatic Differentiation libraries | |
| Assignment(p5) | 10 | PyTorch | |
| Lecture | 10 | Derivative free optimization | CMA-ES |
| Lecture | 11 | Unsupervised learning: feature extraction | |
| Lecture | 11 | Multi-armed bandits | SB Chp 2 |
| Exam | 12 | Midterm exam | |
| Lecture | 12 | Markov-decision processes | SB Chp 3 |
| Assignment(w3) | 12 | MDP | |
| Lecture | 13 | Value Iteration | |
| Assignment(p6) | 13 | Value Iteration | |
| Lecture | 14 | Advanced topics TBD |