Seminar in Algorithmic Methods, Spring 16/17

Haim Kaplan

We will follow chapters of the book on Foundations of data science  by Blum, Hopcroft and Kannan. Please read the appendix of the book for some background.

The content of the seminar has some overlap with the course in machine learning. So people that took machine learning may want to take some other seminar, and should NOT do the lectures on machine learning.

Tentative schedule

1. (Mar 16) An introductory lecture

2. (Mar 23) Chapter 2: High Dimensional Spaces.  Assaf Yifrach, Mai Shevach

3. (Mar 30) Chapter 2: High Dimensional Spaces. Assaf Yifrach, Amit Waisel,

4. (Apr 20, start on March 30) Chapter 3: Best-Fit Subspaces and Singular Value Decomposition (SVD). Noga Morag

5. (Apr 27) Chapter 3: Best-Fit Subspaces and Singular Value Decomposition (SVD). Elad Pardilov,

6. (May 4) Chapter 5: Machine Learning. Tomer Ben-Moshe, Matan Hasson, Dana Sharon, Tami Lavi, Amos Arbiv

7. (May 11) Chapter 5: Machine Learning,

8. (May 18) Chapter 5: Machine Learning,

9. (June 1) Chapter 6: Algorithms for Massive Data Problems:   Streaming and Sketching. Shai Mendel, (notes)

10. (June 8) Chapter 7: Clustering. , Ilia Fallach, Gal Sadeh, Ido Ben-Shaul

11. (Friday June 9, instead of June 15) Chapter 7: Clustering.

12. (June 22) Chapter 8: Random Graphs. Omri Ben Horin, Nadav Goldfarb, Itsik Benishu, David Trabish

13. (June 29) Chapter 8: Random Graphs.