| Group | Title | Slides |
|---|---|---|
| 1 | Stock Market Prediction using k-Nearest Neighbor | PPTX |
| 2 | Restaurant Visitor Forecasting | |
| 3 | Bankruptcy Prediction Using Accrual Variable as Additional Input Feature | PPTX |
| 4 | Predict users' ratings on items | PPTX |
| 5 | Image segmentation | PPTX |
| 6 | YouTube Trending Videos Analysis | PPTX |
| 7 | Use review texts to predict review rating of products | PPTX |
| 8 | Parking Guiding System | PPTX |
| 9 | If clients are able to repay their loan? | PPTX |
| 10 | Air Quality Prediction | PPTX |
| 11 | Predict MLB "Money Line" in sport lottery | PPTX |
| 12 | Fake News Detection | PPTX |
| 13 | Music Generation | PPTX |
| 14 | (Dropped) | |
| 15 | Taiwan Landmark Recognition | PPTX |
| 16 | Capture Video Highlights by Bullet Screen | PPTX |
| 17 | MovieLens Recommender System | PPTX |
| 18 | Predict Students' Academic Performance | PPTX |
| 19 | Steam Game Price Analysis | PPTX |
| 20 | Captcha Verification | PPTX |
| Week | Date | Content | Exercise |
|---|---|---|---|
| 1 | 9/11 | 1. Overview of the course | |
| 2 | 9/18 | 1. Introduction to ML 2. KNN 3. k-means | Exercise 1 (due: 9/24 23:59:59) |
| 3 | 9/25 | 1. Distance measures 2. Entropy 3. Decision tree TA session: Introduction to Python and popular scientific libraries in Python | |
| 4 | 10/2 | 1. Decision tree 2. Linear regression and gradient descent | Exercise 2 (due: 10/15 23:59:59) |
| 5 | 10/9 | 1. Linear regression and regularization (Lasso, Ridge, Elastic-net) 2. Matrix derivatives Invited talk: "Tailoring Video Streaming Service with Data" by Dr. Drake Guan and Dr. Jing-Kai Lou (KKStream) | |
| 6 | 10/16 | 1. Evaluation metrics for regression problem 2. Logistic regression and gradient ascent 3. Precision, recall, ROC curve, and other measures 4. Poisson regression | |
| 7 | 10/23 | SVM | Exercise 3 (due: 11/12 23:59:59) |
| 8 | 10/30 | Midterm project proposal presentation | |
| 9 | 11/6 | 1. Lagrange multiplier 2. Regularized linear regression and classification | |
| 10 | 11/13 | 1. Linear vs Kernel 2. Practical considerations | |
| 11 | 11/20 | 1. Ensemble methods 2. Recommender systems | |
| 12 | 11/27 | Deep neural network | Exercise 4 (due: 12/11 7:59 am) |
| 13 | 12/4 | 1. Convolutional neural network 2. Capsule network | |
| 14 | 12/11 | Recurrent neural network | |
| 15 | 12/18 | Word embedding and graph embedding | |
| 16 | 12/25 | Final project presentation | |
| 17 | 1/1 | Holiday | |
| 18 | 1/8 | Final wrap-up | Final project report (due: 1/8 23:59:59) |