Lecturer: Hossein Hajiabolhassan
The Webpage of the Course: Machine Learning
Data Science Center, Shahid Beheshti University
Learning algorithms are ubiquitous, and are playing an ever increasing role in our daily lives. How are they different from any old algorithm? How can we reason about the ability of an algorithm to “learn from examples”, and classify data it has never seen? We will introduce the formal notions of learnability, generalization, supervised vs. unsupervised learning, etc. The goal is also to introduce the fundamental techniques and models that are central in today’s ML applications.
Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David
Class time and Location:
Saturday and Monday 08:00-10:00 AM (Fall 2018), Room 204/1.
General mathematical sophistication; and a solid understanding of Algorithms, Linear Algebra, and Probability Theory, at the advanced undergraduate or beginning graduate level, or equivalent.
- Homework – 10%
- Midterm – 40%
- Endterm – 50%
I’ll be having office hours for this course on Saturday 10:00 AM–12:00 AM. If this isn’t convenient, email me at email@example.com or talk to me after class.