Course Schedule

Weekday Regular Schedule

Group Type Hours Location
All Lecture Sunday 13:00-16:00 Naftali 001
1 Recitation Wednesday 14:00-15:00 Orenstein 103
2 Recitation Wednesday 15:00-16:00 Orenstein 103
3 Recitation Wednesday 16:00-17:00 Orenstein 111

Detailed Schedule

Lecture Date Topics Lecturer Slides Scribes
1 Oct. 30, 2016 Introduction to the course and to machine learning. K-Nearest Neighbor algorithm Yishay Mansour lecture 1 scribe 1 recitation 1
2 Nov. 6, 2016 PAC model and basic Generalization bounds Yishay Mansour lecture 2 scribe 2 recitation 2
3 Nov. 13, 2016 Generalization bounds: VC dimension, Rademacher Complexity, Model Selection Yishay Mansour lecture 3 scribe 3 recitation 3
4 Nov. 20, 2016 Perceptron algorithm and mistake bound Yishay Mansour lecture 4 scribe 4 recitation 4
5 Nov. 27, 2016 Support Vector Machines (SVM) Amir Globerson lecture 5 scribe 5 recitation 5
6 Dec. 4, 2016 Kernels Amir Globerson lecture 6 scribe 6 recitation 6
7 Dec. 11, 2016 Stochastic Gradient Descent and Deep Learning Amir Globerson lecture 7 scribe 7 recitation 7
8 Dec. 18, 2016 Decision Trees Yishay Mansour lecture 8 scribe 8 recitation 8
Dec. 25, 2016 SVD (Hanukkah) recitation 9
9 Jan. 1, 2017 Boosting and ensemble methods Yishay Mansour lecture 9 scribe 9 recitation 10
10 Jan. 8, 2017 Regression and PCA Amir Globerson lecture 10 scribe 10 recitation 11
11 Jan. 15, 2017 Clustering and Generative Models Amir Globerson lecture 11 (no scribe) recitation 12
12 Jan. 22, 2017 Gaussian mixture model (GMM) and Expectation Maximization (EM) Amir Globerson lecture 12 scribe 12 recitation 13 (pdf)
13 Jan. 29, 2017 Graph Based Methods and Summary Amir Globerson lecture 13

Previous years' scribes are available here.

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