Professor
Jonathan Kao
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Most Helpful Review
Spring 2023 - I love Jonathan Kao. His lectures are very clear—with amazing annotated notes. Concepts that may seem confusing—Kao has a unique ability to make them seem approachable and common sense-like. In office hours, he is always willing to take questions, talk about the course, or just life in general. I've had great conversations with him regarding the existence of free will. Not many professors are that engaged with their students. One minor criticism I have of Kao is how he takes questions in lecture. He indulges in almost every single question, which slows down lecture tremendously. It's great he wants to resolve any unanswered questions, but it's just too many. (It's also evident that some students ask questions just to make them seem smarter to the professor, but that's another concern.) I feel like the professor can fix this by setting expectations for questions at the beginning of the course. If you feel like your question helps everyone in class, feel free to ask it in lecture. If not, ask it during office hours. Overall though, great professor. I love Tonmoy Monsoor. Super knowledgeable TA, always willing to help during discussion, holds great review sessions.
Spring 2023 - I love Jonathan Kao. His lectures are very clear—with amazing annotated notes. Concepts that may seem confusing—Kao has a unique ability to make them seem approachable and common sense-like. In office hours, he is always willing to take questions, talk about the course, or just life in general. I've had great conversations with him regarding the existence of free will. Not many professors are that engaged with their students. One minor criticism I have of Kao is how he takes questions in lecture. He indulges in almost every single question, which slows down lecture tremendously. It's great he wants to resolve any unanswered questions, but it's just too many. (It's also evident that some students ask questions just to make them seem smarter to the professor, but that's another concern.) I feel like the professor can fix this by setting expectations for questions at the beginning of the course. If you feel like your question helps everyone in class, feel free to ask it in lecture. If not, ask it during office hours. Overall though, great professor. I love Tonmoy Monsoor. Super knowledgeable TA, always willing to help during discussion, holds great review sessions.
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Most Helpful Review
Winter 2024 - I think the winter 2024 offering of C147 was very similar to the reviews I've read in the past: - There were some very hard/long assignments. However if you used Piazza you should be fine - The class was a bit more mathy in the beginning than compared to the end - The professor was excellent! There are also several comments complaining about the "slow pace" of the class, "over-enrollment" and new 50% Midterm Weight. I'd like to address those and what my views are as a fellow student: Personally, as someone who skipped M146 and went straight into C147, I appreciated that the professor reviewed some of the more fundamental aspects of DL in the beginning of the course. Furthermore, I'd like to point out that this is after all, a graduate class. Grad Students often don't get to take the particular sequence of courses at UCLA prior to coming here, so it make sense that the professor do some review in the very beginning to make sure everyone is on the same page. For those that were interested in "practical" machine learning (ie learning one of the popular ML libs), the final project was an opportunity to do that. Personally, I think this class is not a torch-bootcamp and rightfully so: C147 focus on more fundamental knowledge and the theory side of DL. There are also other more coding-heavy classes offered (CS188 in W24 for example) that one can take to gain that kind of experience. While it is true this was a very big class, personally between taking the class vs not taking the class I'd always choose the former. I definitely appreciated the effort the teaching staff put into making the logistics work for such a large class! Finally, the median for the class was an A- (partly due to grad class, also because Kao doesn't grade harsh). I really don't understand why people complain about grades at this point considering this is an upper-div and the midterm median was a B.
Winter 2024 - I think the winter 2024 offering of C147 was very similar to the reviews I've read in the past: - There were some very hard/long assignments. However if you used Piazza you should be fine - The class was a bit more mathy in the beginning than compared to the end - The professor was excellent! There are also several comments complaining about the "slow pace" of the class, "over-enrollment" and new 50% Midterm Weight. I'd like to address those and what my views are as a fellow student: Personally, as someone who skipped M146 and went straight into C147, I appreciated that the professor reviewed some of the more fundamental aspects of DL in the beginning of the course. Furthermore, I'd like to point out that this is after all, a graduate class. Grad Students often don't get to take the particular sequence of courses at UCLA prior to coming here, so it make sense that the professor do some review in the very beginning to make sure everyone is on the same page. For those that were interested in "practical" machine learning (ie learning one of the popular ML libs), the final project was an opportunity to do that. Personally, I think this class is not a torch-bootcamp and rightfully so: C147 focus on more fundamental knowledge and the theory side of DL. There are also other more coding-heavy classes offered (CS188 in W24 for example) that one can take to gain that kind of experience. While it is true this was a very big class, personally between taking the class vs not taking the class I'd always choose the former. I definitely appreciated the effort the teaching staff put into making the logistics work for such a large class! Finally, the median for the class was an A- (partly due to grad class, also because Kao doesn't grade harsh). I really don't understand why people complain about grades at this point considering this is an upper-div and the midterm median was a B.
Most Helpful Review
Fall 2022 - This is a interesting seminar that introduces you to the research of biomedical devices that interface directly with neurons (brain machine interfaces or BMIs), and you get to hear Kao discuss about his research as well. The workload is very light, lecture notes are posted on BruinLearn, and only has a final project (fairly easy to complete) assigned during the last two weeks of the quarter that for my year is completed in MATLAB (Kao has plans to update this to Python like he did for assignments for his ECE 102 class).
Fall 2022 - This is a interesting seminar that introduces you to the research of biomedical devices that interface directly with neurons (brain machine interfaces or BMIs), and you get to hear Kao discuss about his research as well. The workload is very light, lecture notes are posted on BruinLearn, and only has a final project (fairly easy to complete) assigned during the last two weeks of the quarter that for my year is completed in MATLAB (Kao has plans to update this to Python like he did for assignments for his ECE 102 class).
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Most Helpful Review
Spring 2023 - A great course with a great lecturer and TAs. The lectures are well prepared and Prof. Kao is really good at teaching. He's happy to stop anytime and answer your questions. TAs are very helpful in the discussions and OH. The exams are fair, and do please attend the midterm and final review held by the TA! The topics are very similar to what will appear in the exam so you definitely should spend enough time reviewing these topics. If you are interested in the neuroscience and have a strong knowledge base of probability, linear algebra and Python, the course is a perfect choice. A little bit of matrix calculus is involved but truse me, they just look scary. 40% 6 homeworks, 25% midterm, 35% final.
Spring 2023 - A great course with a great lecturer and TAs. The lectures are well prepared and Prof. Kao is really good at teaching. He's happy to stop anytime and answer your questions. TAs are very helpful in the discussions and OH. The exams are fair, and do please attend the midterm and final review held by the TA! The topics are very similar to what will appear in the exam so you definitely should spend enough time reviewing these topics. If you are interested in the neuroscience and have a strong knowledge base of probability, linear algebra and Python, the course is a perfect choice. A little bit of matrix calculus is involved but truse me, they just look scary. 40% 6 homeworks, 25% midterm, 35% final.
Most Helpful Review
Winter 2021 - Be prepared to spend 20+ hours a week on the homework assignments. I learned a ton from this course. It makes it to where AI/ML is not a black box anymore. You can understand how things are working and how it all comes back to the math. The lectures are very good. The professor and TAs are very helpful. It is a great course which I would recommend if you are single and have the time.
Winter 2021 - Be prepared to spend 20+ hours a week on the homework assignments. I learned a ton from this course. It makes it to where AI/ML is not a black box anymore. You can understand how things are working and how it all comes back to the math. The lectures are very good. The professor and TAs are very helpful. It is a great course which I would recommend if you are single and have the time.
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