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.