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
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