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Professor
Suhas Diggavi
Most Helpful Review
Winter 2016 - Toughest class I've ever taken at UCLA. His exams are also one of the hardest exams I've ever taken. I wish someone told me how hard this class was before I got into it. His curve is super great though, so even though you feel like youre failing, youre actually in like a B- range.
Winter 2016 - Toughest class I've ever taken at UCLA. His exams are also one of the hardest exams I've ever taken. I wish someone told me how hard this class was before I got into it. His curve is super great though, so even though you feel like youre failing, youre actually in like a B- range.
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Most Helpful Review
Spring 2024 - I agree with the last comment. Professor Diggavi has to be one of the greats in machine learning and definitely cares about his students. The truth is, this class was all just very rigorous mathematical concepts and proofs (even on exams) and Professor Diggavi does not shy away from that. Unfortunately, I do not think my coursework adequately prepared me to take this class, but looking back, CS M148 now looks like a walk in the park. I don't think taking this course deters me at all from taking future data science coursework (especially at the graduate level), but it was definitely a wake-up call that the theory of ML is quite complex and is no easy task. Weirdly, now I want to get better at math to better understand this stuff. Relevant Prerequisite knowledge that I had going into course: 33A, PIC 10C, MATH 170E, MATH 170S, CS M148, STATS 115. I would probably recommend some more rigorous proof based math (e.g., MATH 115) to get more out of the class...this stuff is all linear algebra.
Spring 2024 - I agree with the last comment. Professor Diggavi has to be one of the greats in machine learning and definitely cares about his students. The truth is, this class was all just very rigorous mathematical concepts and proofs (even on exams) and Professor Diggavi does not shy away from that. Unfortunately, I do not think my coursework adequately prepared me to take this class, but looking back, CS M148 now looks like a walk in the park. I don't think taking this course deters me at all from taking future data science coursework (especially at the graduate level), but it was definitely a wake-up call that the theory of ML is quite complex and is no easy task. Weirdly, now I want to get better at math to better understand this stuff. Relevant Prerequisite knowledge that I had going into course: 33A, PIC 10C, MATH 170E, MATH 170S, CS M148, STATS 115. I would probably recommend some more rigorous proof based math (e.g., MATH 115) to get more out of the class...this stuff is all linear algebra.