This is a prediction of what will be covered each week, but the schedule is subject to change (hopefully none or minimal) as the course progresses.
| Week of | Day | Lecture Topic (Click for recording) | Homework Due (Days from lecture to homework) |
|---|---|---|---|
| 14-Sep | Mon | [Classes begin Wednesday, 9/16] | |
| Wed | Intro to Class | ||
| Fri | Intro to ML | ||
| 21-Sep | Mon | Intro to ML (continued) | HW0 - Setup (5 days) |
| Wed | Decision Trees | ||
| Fri | Decision Trees (continued) | ||
| 28-Sep | Mon | Probability | |
| Wed | Bayes’ Theorem | ||
| Fri | Naive Bayes, MAP, and MLE | ||
| 5-Oct | Mon | Measuring Distance | |
| Wed | k-Nearest Neighbors | ||
| Thu | HW1 - Decision Trees (13 days) | ||
| Fri | Collaborative Filtering | ||
| 12-Oct | Mon | k-Means Clustering | |
| Wed | Gaussian Mixture Models | ||
| Thu | HW2 - Measuring Distance, k-Nearest Neighbors (8 days) | ||
| Fri | Gaussian Mixture Models (continued) | ||
| 19-Oct | Mon | Linear Regression | |
| Wed | Logistic Regression | ||
| Thu | |||
| Fri | Support Vector Machines | ||
| Sat | HW3 - k-Means Clustering, GMMs (8 days) | ||
| 26-Oct | Mon | Local Search | |
| Wed | Gradient Descent, Regularization | ||
| Thu | HW4 - Linear Regression (6 days due to Friday assignment) | ||
| Fri | Genetic Algorithms | ||
| 2-Nov | Mon | Boosting | |
| Wed | Crowd Sourcing, Active Learning | ||
| Thu | |||
| Fri | Reinforcement Learning | HW5 - Gradient Descent, Regularization (9 days) | |
| 9-Nov | Mon | [Lecture cancelled] | |
| Wed | Reinforcement Learning (continued) + Perceptrons recorded outside of class | ||
| Thu | |||
| Fri | Neural Networks, Deep Learning | ||
| 16-Nov | Mon | Probability Density Function + Much More on MLE | |
| Wed | Experimental Validation | ||
| Fri | Dos and Don’ts of ML, Data Handling and Considerations, and ML in academia vs. ML in the industry + Review or general discussion about ML and AI | ||
| 23-Nov | Mon | Q&A about Final (Final moved to 7-Dec, during Exam Week) | HW6 - Reinforcement Learning (12 days) + HW7 - Neural Networks, Deep Learning (9 days) |
| Tue | HW8 - Presentation of a Research Paper (not tied to a lecture (Presentation not live; due on Canvas as short video) | ||
| Wed | [Thanksgiving holiday begins] | ||
| Fri | [Thanksgiving holiday continues] | ||
| 30-Nov | Mon | Review or general discussion about ML and AI | |
| 1-Dec | Wed | [Last day of classes, but we don’t have class] | |
| 7-Dec | Tue | Final (Exam 3PM - 5 PM on Canvas while being on Zoom OR project due OR video of paper presentation due) |