Professor
Suhas Diggavi
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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 - Professor Diggavi is definitely an expert in the field of ML, as he would often share with us stories behind the development of ML algorithms. Even though his lectures are dry and math-heavy, he is able to explain most of the abstract concepts clearly. Contrary to popular opinion, I actually appreciated the time and effort he put into going through the mathematical derivations behind the theorems and algorithms. Exams were on the tough end but fair - just make sure to include all the key concepts + proofs in your cheatsheet, and fully understand the practice exam. However, homework specs can be confusing at times, with a couple of mistakes here and there. Fortunately, the TAs (esp Sadik) were really responsive on Campuswire to clarify any doubts we had. Overall, I do think this is a well-structured course, especially if you are keen to learn more about the math behind ML, which complements well with the more applied ML courses like ECE C147 and CS 162/163. I do think that taking 115A and 170S concurrently with this class helped me a lot. As mentioned in the previous comments, classes like PIC 16A, Math 115A, Math 170S and CS M148 are helpful pre-requisites.
Spring 2024 - Professor Diggavi is definitely an expert in the field of ML, as he would often share with us stories behind the development of ML algorithms. Even though his lectures are dry and math-heavy, he is able to explain most of the abstract concepts clearly. Contrary to popular opinion, I actually appreciated the time and effort he put into going through the mathematical derivations behind the theorems and algorithms. Exams were on the tough end but fair - just make sure to include all the key concepts + proofs in your cheatsheet, and fully understand the practice exam. However, homework specs can be confusing at times, with a couple of mistakes here and there. Fortunately, the TAs (esp Sadik) were really responsive on Campuswire to clarify any doubts we had. Overall, I do think this is a well-structured course, especially if you are keen to learn more about the math behind ML, which complements well with the more applied ML courses like ECE C147 and CS 162/163. I do think that taking 115A and 170S concurrently with this class helped me a lot. As mentioned in the previous comments, classes like PIC 16A, Math 115A, Math 170S and CS M148 are helpful pre-requisites.
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