Lectures
| Lecture # | Slides | Recording |
|---|---|---|
| 1 | Introduction | 1/13/2022 |
| 2 | Introduction (continue) | 1/20/2022 |
| 3 | K-Nearest Neighbors | 1/25/2022 |
| 4 | Perceptrons | 1/27/2022 |
| 5 | Perceptrons (continue) | 2/1/2022 |
| 6 | Class canceled (winter storm) | 2/3/2022 |
| 7 | Generative Models | 2/8/2022 |
| 8 | Probabilistic Reasoning | 2/10/2022 |
| 9 | Bayes' Nets | 2/15/2022 |
| 10 | Naive Bayes | 2/16/2022 |
| 11 | Logistic Regression | 2/22/2022 |
| 12 | Gradient Descent | 2/24/2022 |
| 13 | Linear Regression | 3/1/2022 |
| 14 | Support-Vector Machines | 3/3/2022 |
| 15 | Risk Minimization | 3/7/2022 |
| 16 | Bias and Variance | 3/10/2022 |
| - | Spring break | 2/14--18/2022 |
| 17 | ML Debugging | 3/22/2022 |
| 18 | Kernelization | 3/24/2022 |
| 19 | Kernel Machines | 3/29/2022 |
| 20 | Decision Trees | 3/31/2022 |
| 21 | Bootstrap Aggregation | 4/5/2022 |
| 22 | Gradient Boosting | 4/7/2022 |
| 23 | Midterm Exam | 4/12/2022 |
| 24 | Adaptive Gradient Boosting | 4/14/2022 |
| 25 | Artificial Neural Network | 4/19/2022 |
| 26 | Backpropagation | 4/21/2022 |
| 27 | Cross-Entropy and CNNs | 4/26/2022 |
| 28 | Generativ Modeling | 4/28/2022 |
| 29 | Derivative Free Methods | NA |