Lecture is on Tuesday and Thursday from 3:30PM - 4:45PM in Thornton Hall E316.
The lecture schedule below is tentative and is continually subject to change; We will move at whatever pace we find comfortable.
| Index | Date | Assignments | References | Notes |
|---|---|---|---|---|
| L1 | Tu - Aug. 26 | Lecture slides | . | Introduction/Logistics |
| L2 | Th - Aug. 28 | Lecture slides | Handout | Linear Algebra Review |
| SECTION I: Supervised Regression | ||||
| L3 | Tu - Sept. 2 | Lecture slides | HW1 out; | Linear Prediction |
| L4 | Th - Sept. 4 | Lecture slides | Handout | Optimization for Learning Linear Regression Model; |
| L5 | Tu - Sept. 9 | Lecture slides | . | Newton's Method; Nonlinear Prediction; Local Linear Prediction; |
| L6 | Th - Sept. 11 | Lecture slides | . | Linear Prediction with Regularization, Sparse Regularization, Feature Selection. To READ: ESL book / Ch3.4 except degree of freedom part. |
| L7 | Tu - Sept. 16 | Lecture slides | HW1 due ; HW2 out; | Review of five regression models we have learned. |
| SECTION II: Supervised Classification | ||||
| L8 | Th - Sept. 18 | Lecture slides | . | Review of five regression models / k-folds Cross-validation / Support Vector Machine (SVM) |
| L9 | Th - Sept. 25 | Lecture slides | . | Support Vector Machine (SVM) |
| L10 | Fri - Sept. 26 | Lecture slides | 8:30-9:45am / Same classroom | Support Vector Machine (SVM) |
| L11 | Tu - Sept. 30 | Lecture slides | Handout | Support Vector Machine (SVM) review and pratical guide of using SVM |
| L12 | Th - Oct. 2 | Lecture slides | HW2 due ; | Probability Review |
| L13 | Tu - Oct. 7 | Lecture slides | . | Naive Bayes Classifier |
| L14 | Th - Oct. 9 | Lecture slides | . | Naive Bayes Classifier for text categorization |
| E | Tu - Oct. 14 | Reading Day | HW3,HW4 out ; | HW4-sample-midterm-questions Out; |
| L15 | Th - Oct. 16 | Lecture slides | Handout of MLE | Logistic Regression / |
| L16 | Tu - Oct. 21 | Lecture slides | . | Gaussian Naive Bayes / Prototype-based Prediction/ |
| L17 | Th - Oct. 23 | Lecture slides | HW3 due (S) | Review / Bias-Variance Tradeoff |
| L18 | Tu - Oct. 28 | Lecture slides (L17 cont.) | . | Review / Bias-Variance Tradeoff |
| E | Th - Oct. 30 | Midterm | HW4 due; | . |
| L19 | Tu - Nov. 4 | Lecture slides | . | Decision Tree / Ensemble |
| L20 | Th - Nov. 6 | Lecture slides | HW5 out (4501-Programming / 6501-Proposal) | Neural Network / Deep Learning ( Tutorial Handout - Optional) |
| L21 | Tu - Nov. 11 | Lecture slides | . | NN, Backprop / Deep Learning |
| L22 | Th - Nov. 13 | Lecture slides | . | Feature Selection |
| SECTION III: Unsupervised Learning | ||||
| L23 | Tu - Nov. 18 | Lecture slides | HW6 (Wed) out; | PCA and EigenFace for Image-based Face Recognition |
| L24 | Th - Nov. 20 | Lecture slides | HW5 due (Sun) | Hierarchical clustering |
| L25 | Tu - Nov. 25 | Lecture slides | . | K-means Clustering / GMM-EM |
| E | Th - Nov. 27 | NO CLASS (Thanksgiving Holiday) | . | . |
| L26 | Tu - Dec. 2 | Final Review | HW6 due (Wed by 5pm) | . |
| E | Th - Dec. 4 | Final exam (in class exam) | . | . |