Reviews of professor Chris De Sa

Rating: 5.0/5, difficulty: 4.0/5
Great lectures, delivered with a lot of passion and vigor. Clear explanations. He is a genius so often time you have to ask him for clarification when he jumps 5 steps in a derivation, but other than that he's great. Exams are hard, but he gives a nice curve

Rating: 5.0/5, difficulty: 4.0/5
De Sa was excellent in Principles of Large Scale Machine Learning. His lectures were clear, organized (notes on website), and very rigorous. I came out of the class feeling like I had strong skills in supervised learning, and good intuition for any related to ML or optimization. Material is used in practice in and is cutting edge for a ugrad course

Rating: 5.0/5, difficulty: 5.0/5
He is very good at lecturing and clearly explaining concepts. However, CS 4787 has many assignments; I believe he managed to squeeze in 6 programming assignments, 6 problem sets, 5 paper readings, and 2 exams into a 15 week semester.

Rating: 5.0/5, difficulty: 3.0/5
De Sa was super nice, helpful at his OH, and gave awesome print outs of his lecture notes. The programming assignments (in python) build off each other so sometimes your just copy/pasting code and didn't take too long to complete. Overall I learned ML much better than CS4780 and used many of the concepts at my AI/ML internship, highly recommend.

Rating: 3.0/5, difficulty: 2.0/5
Chris de Sa is the Josh Peck of the CS department, beware you could get a newscaster incident. Also cares a lot about ML, and is invested in students learning the material.

Rating: 5.0/5, difficulty: 3.0/5
Most concepts taught in this course are very very useful. If you want to pursue your career as an ML/DL engineer/researcher, this course is basically mandatory, although many concepts are indeed difficult to understand.

Rating: 5.0/5, difficulty: 3.0/5
Hard core course but moderate exam (for the year that I'm taking).

Rating: 5.0/5, difficulty: 3.0/5
Highly recommend 6787 w/ De Sa for anyone familiar w/ ML who wants to get acquainted with how ML research is conducted on all sides---statistical to hardware. Took away a lot of messages and ended up thinking about/reading material outside of class. He lets students lead paper review lectures, and mainly moderated/clarified things.