Course Schedule
Weekday Regular Schedule
| Group | Type | Hours | Location |
|---|---|---|---|
| All | Lecture | Sunday 13-15 | Dan David 110 |
| 1 | Recitation | Sunday 15-16 | Dan David 110 |
| 2 | Recitation | Sunday 16-17 | Dan David 110 |
Detailed Schedule
| Lecture | Date | Lecture topics | lecturer | Lecture slides | Scribes |
| 1 | Oct. 26, 2014 | Introduction to the course and to machine learning. K-Nearest Neighbor algorithm, and K-means algorithm | Lior Wolf | Lecture Recitation (Week 1) |
|
| 2 | Nov. 2, 2014 | Bayesian Inference | Eran Halperin | Slides (lessons 2-3) | Lecture Recitation (Week 2) |
| 3 | Nov. 9, 2014 | Gaussian Mixture Model (GMM) and Expectation Maximization (EM) | Eran Halperin | Lecture Recitation (Week 3) Recitation (Week 4) |
|
| 4 | Nov. 16, 2014 | Probably Approximately Correct (PAC) model. including generalization bounds and model selection. |
Lior Wolf | Slides | Lecture Recitation (Week 5) |
| 5 | Nov. 23, 2014 | Basic hyperplane algorithms: Perceptron and Winnow. | Lior Wolf | Slides | Lecture Recitation (week 6) |
| 6 | Nov. 30, 2014 | Support Vector Machines (SVM) | Lior Wolf | Slides | Lecture Recitation (Week 7) |
| 7 | Dec. 7, 2014 | Kernels | Lior Wolf | Slides | Lecture Recitation (Week 8) |
| 8 | Dec. 14, 2014 | Boosting weak learners to strong learners: AdaBoost | Lior Wolf | Slides | Lecture Recitation (Week 9) |
| 9 | Dec. 28, 2014 | Regression problems | Eran Halperin | Slides | Lecture Recitation (Week 10) |
| 10 | Jan. 4, 2015 | Principle Component Analysis (PCA) | Eran Halperin | Slides | Lecture Recitation (Week 11) |
| 11 | Jan. 11, 2015 | Decision trees | Lior Wolf | Slides | Lecture |
| 12 | Jan. 18, 2015 | Decision trees pruning and random forests | Lior Wolf | Slides | Lecture |
| 13 | Jan. 25, 2015 | Applications | Lior Wolf | Slides | Recitation (Week 13) |
Last Year's Scribes
| Lecture | Date | Lecture topics | lecturer | Lecture slides | Scribes |
| 1 | Oct. 13, 2013 | Introduction to the course and to machine learning. K-Nearest Neighbor algorithms, and k-means algorithms | Lior Wolf | Lecture Recitation Recitation Figures |
|
| 2 | Oct. 20, 2013 | Bayesian Inference | Eran Halperin | BayesianInferenceLectures1-2.pdf. | Lecture Recitation |
| 3 | Oct. 27, 2013 | Gaussian Mixture Model (GMM) and Expectation Maximization (EM) | Eran Halperin | Lecture Recitation |
|
| 4 | Nov. 3, 2013 | Probably Approximately Correct (PAC) model. including generalization bounds and model selection. |
Yishay Mansour | Lecure 4 PAC | Lecture Recitation |
| 5 | Nov. 10, 2013 | Basic hyperplane algorithms: Perceptron and Winnow. | Yishay Mansour | Online Learning | Lecture Recitation |
| 6 | Nov. 17, 2013 | Support Vector Machines (SVM) | Lior Wolf | SVM | Lecture Recitation |
| 7 | Nov. 24, 2013 | Kernels | Lior Wolf | Kernels (we only got as far as Kernel SVD) | Lecture Recitation |
| 8 | Dec. 8, 2013 | Boosting weak learners to strong learners: AdaBoost | Yishay Mansour | Boosting | Lecture Recitation |
| 9 | Dec. 15, 2013 | Regression problems | Eran Halperin | Regression | Lecture Recitation |
| 10 | Dec. 22, 2013 | Principle Component Analysis (PCA) | Eran Halperin | PCA | Lecture Recitation |
| 11 | Dec. 29, 2013 | Finish PCA start Decision trees |
Yishay Mansour | Decision Trees | Lecture |
| 12 | Jan. 5, 2014 | Decision trees pruning and random forest | Yishay Mansour | Decision Trees - part 2 | Lecture Recitation |
| 13 | Jan. 12, 2014 | Applications | Lior Wolf | Applications |