| Group | Title | Slides |
|---|---|---|
| 1 | Gesture Recognition | |
| 2 | Movie Rating Prediction | PPTX |
| 3 | Image Recognition | PPTX |
| 4 | Bankruptcy | PPTX |
| 5 | Life Prediction | PPTX |
| 6 | Employee Leave Prediction | PPTX |
| 7 | Kidney Eyes | PPTX |
| 8 | Ads Recommendation | PPTX |
| 9 | Dynamics of Terrorism Regularity | PPTX |
| 10 | How to be Happy | PPTX |
| 11 | NBA Lottery | PPTX |
| 12 | Highway Traffic Prediction | PPTX |
| 13 | User Online Browsing and Shopping Behavior Pattern | PPTX |
| 14 | Design a User Friendly Webpage | PPTX |
| 15 | Personality Prediction | PPTX |
| 16 | DoS Attack Prediction | PPTX |
| 17 | Bus Travel Time Prediction | PPTX |
| 18 | GDP Prediction | PPTX |
| 19 | Stock Price Prediction | PPTX |
| 20 | Lung Adenocarcicoma Classification | PPTX |
| Week | Date | Content | Exercise |
|---|---|---|---|
| 1 | 2/13 | Overview of the course | Exercise 0 (due: none) |
| 2 | 2/20 | 1. Introduction to ML 2. KNN 3. k-means | Exercise 1 (due: 3/5 23:59:59) |
| 3 | 2/27 | Holiday | |
| 4 | 3/6 | 1. Distance measures 2. Decision tree | Exercise 2 (due: 3/19 23:59:59) |
| 5 | 3/13 | 1. Entropy 2. Decision tree 3. Linear regression | |
| 6 | 3/20 | 1. Matrix derivatives 2. Linear regression and gradient descent | |
| 7 | 3/27 | 1. Linear regression (Lasso, Ridge, Elastic-net) 2. Logistic regression | Exercise 3 (due: 4/9 23:59:59) |
| 8 | 4/3 | Holiday | |
| 9 | 4/10 | Invited talks | |
| 10 | 4/17 | Midterm project proposal presentation | |
| 11 | 4/24 | 1. Logistic regression and gradient ascent 2. Precision, recall, ROC curve, and other measures | |
| 12 | 5/1 | 1. SVM 2. Lagrange Multiplier | Exercise 4 (due: 5/14 23:59:59) |
| 13 | 5/8 | 1. SVM 2. Regularized linear regression and classification 3. Linear vs Kernel | |
| 14 | 5/15 | 1. Practical considerations 2. Ensemble methods | |
| 15 | 5/22 | 1. Ensemble methods 2. Reinforcement learning | |
| 16 | 5/29 | Holiday | |
| 17 | 6/5 | Final project presentation | |
| 18 | 6/12 | Deep learning | Final project report due |