Course Schedule

Weekday Regular Schedule

Group Type Hours Location
All Lecture Sunday 13:00-15:00 The Porter School of Environmental Studies 101
1 Recitation Wednesday 14:00-15:00 Shenkar 204
2 Recitation Wednesday 15:00-16:00 Shenkar 204

Detailed Schedule

Lecture Date Lecture topics Lecturer Lecture slides Scribes
1 Oct. 18, 2015 Introduction to the course and to machine learning. K-Nearest Neighbor algorithm, and K-means algorithm Lior Wolf Lecture
Recitation
2 Oct. 25, 2015 Statistical Inference Eran Halperin Slides Lecture
Recitation
3 Nov. 1, 2015 Gaussian Mixture Model (GMM) and Expectation Maximization (EM) Eran Halperin Slides Lecture
Recitation
4 Nov. 8, 2015 Probably Approximately Correct (PAC) model, including generalization bounds and model selection Tomer Galanti Slides Lecture
Recitation
5 Nov. 15, 2015 Basic hyperplane algorithms: Perceptron and Winnow. Lior Wolf Slides Lecture
Recitation
6 Nov. 22, 2015 Support Vector Machines (SVM) Lior Wolf Slides Lecture
Recitation
7 Nov. 29, 2015 Kernels Lior Wolf Slides Lecture
Recitation
8 Dec. 6, 2015 Boosting weak learners to strong learners: AdaBoost Lior Wolf Slides Lecture
Recitation
9 Dec. 13, 2015 Margin-Perceptron Recitation
10 Dec. 20, 2015 Regression Eran Halperin Slides Lecture
Recitation
11 Dec. 27, 2015 PCA Eran Halperin Slides Lecture
Recitation
12 Jan. 3, 2016 Decision Trees Lior Wolf Slides Lecture
Recitation (student summary)
13 Jan. 12, 2016 Decision Trees pruning and random forests Lior Wolf Slides Lecture
Recitation (partial)
Recitation (student summary)
Recitation (student summary)
Recitation (student summary)

Last Year's Scribes

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)
Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License