| Sequence | Date | Content | Exercise |
|---|---|---|---|
| 1 | 6/28 | 1. Overview of the course | |
| 2 | 6/29 | 1. Introduction to ML 2. KNN 3. k-means | |
| 3 | 7/5 | 1. Distance measures 2. Entropy 3. Decision tree | |
| 4 | 7/6 | 1. Decision tree 2. Matrix derivatives | |
| 5 | 7/12 | Linear regression and regularization (Lasso, Ridge, Elastic-net) | |
| 6 | 7/13 | 1. Logistic regression and gradient ascent 2. Precision, recall, ROC curve, PR cure, and other measures | |
| 7 | 7/19 | 1. Evaluation metrics for multi-class classification 2. SVM | |
| 8 | 7/20 | 1. SVM 2. Regularized linear regression and classification | |
| 9 | 7/26 | 1. Linear SVM with poly-2 terms vs Polynomial Kernel SVM 2. Practical considerations | First competition (50%) Competition ends and report due: 8/11 23:59:59 |
| 10 | 7/27 | Deep neural network | |
| 11 | 8/2 | 1. Multi-layer perceptron 2. Convolutional neural network | |
| 12 | 8/3 | Convolutional neural network | |
| 13 | 8/9 | 1. Convolutional neural network 2. Recurrent neural network | |
| 14 | 8/10 | Associated Learning | |
| 15 | 8/16 | Recommender systems | Second competition (50%) Competition ends and report due: 9/6 23:59:59 |
| 16 | 8/17 | 1. Differentiating regularization weight 2. Factorization Machine and Field-aware Factorization Machine | |
| 17 | 8/23 | 1. Learning to rank 2. Common pitfalls in applied machine learning | |
| 18 | 8/24 | 1. Common pitfalls in applied machine learning 2. Case studies |