I can see where the negative reviews come from regarding the course content since ppl would expect AI courses to be modern and fun instead of theories. While I agree with that, I do want to add my personal thoughts regarding the problem. There are also other professors besides prof. Gu who teach this course and cs department requires them to teach the same materials (otherwise it would be unfair for both teachers and students in different quarters). this intro level AI course was designed years ago and ofc it is a little outdated, but the content can hardly be changed unless the department decides to. I guess for ppl complaining here, it would be better if you talk to cs dept directly instead of giving a low rating for some professors...
Regarding the professor, I took the course when the pandemic hit in 2020 and everything was a mess. I think the professor is knowledgeable and cared a lot about course quality and did a great job accommodating students' needs. I do agree that sometimes the slides are too brief and the textbook definitely gives a more thorough explanation. BUT that is based on if you don't listen to the lecture at all and just reading the slides. Based on my personal experience, it is easier to understand the materials when I went to the lecture with professor's demo.
For TAs, I would agree that they were not that helpful comparing with TAs from other courses. but I do not think they were being lazy (at least mine wasn't) Their speaking skills are not too good so it's difficult to understand, but they were willing to stay after discussion with me to make sure my concerns were resolved.
The HWs and tests are doable as other comments said.
In general, I think this course is a descent intro-level AI course that shows/prepares you the fundamentals behind the fancy side of AI/ML. I also consider it as a good elective with very manageable workload and easy A.

I took the class when covid came and the final exam turned to optional. Professor Gu explained concepts clearly and willing to accommodate student's needs under covid. I went to his office hour couple of times and the professor is helpful and friendly. The TA's sessions are absolutely helpful. The content of the course is indeed a little bit out of date due to the design of our curriculum, which is the little flaw of most of the cs courses. But the part near the end of the class is quite interesting and useful. Take the course if you can! Highly recommended!

Gu is a very knowledgeable prof and cares a lot about his students. He is nice during office hours, very approachable, and is pretty easy to understand when trying to explaining concepts and answering questions. He lectures with slides, which are super clear and helpful. Exams are extremely fair as long as you attend most of the lectures and I would recommend checking the slides to review the important topics for your exam. Highly recommend this class with prof Gu.

Although Prof. Gu is not so good at teaching at the beginning, I can see his teaching skills visually improving throughout the quarter.

I have to say that the exam is well designed. The question is multiple choices but it covers almost everything in the PowerPoint and in class.

Did not enjoy this class too much. The content felt really really slow at the beginning, spending a ton of time on searches that were mostly already covered in CS 180 (which is a prerequisite of this class anyway), and it never felt like we learned anything that was really interesting aside from Bayesian networks which was covered in literally the last two lectures. I made the mistake of not really knowing what the curriculum was going to be before taking the class, so if you're thinking about taking this class because you see that it's about AI and you go OOH AI I'd recommend you take a look at one of the old class websites and take a look over the slides which should be posted, and see if it looks interesting because you might not be taking exactly what you think you are.

The tests are multiple choice which make them not too hard, but also makes it hard to learn anything from the test.

The projects are somewhat interesting I think? It would be nice to have more consistent homeworks though, so that we can exercise the concepts we learned in class. For example, I think that we did not have nearly enough practice solving things like alpha-beta pruning and propositional logic/first order logic problems. The projects aren't too tough though, and are for the most part pretty cool.

If you're looking for a super interesting GE, I wouldn't really recommend this class, but if you're looking for something low effort and are just trying to get by, this is one of the easier CS upper divs to be able to do that with.

Quanquan is a nice professor but his class is just VERY BORING. I understand he tries really hard to explain the concepts but I still think they were really confusing. Quanquan uses slides in his class but I find the textbook to be more useful than the lectures. The tests weren't too bad. I had plenty of time to do each question and enough time to double check my answers. You really just need to understand the things he puts on the slides to get a good grade. The assignments weren't too bad. However the first 4 assignments are LISP coding. If you hate LISP then don't take this class cuz it'll be pretty painful. Also, I thought the coding assignments were pretty outdated. Quanquan said no one really uses LISP in the industry nowadays but somehow we are still using and learning about it.

However, I think the biggest problem of this class was the TAs. They were extremely unhelpful. I went to OH several times to ask about the assignments, and they were never prepared to answer them. They were unclear about explaining the spec and requirements and they don't really answer the questions on Piazza. Also, they thought it was a good idea to do rotated discussions (host only one discussion each week instead of hosting one per TA). Which means if one TA is better at explaining the material, we only get to meet that TA once every three weeks. I personally hate this idea and I think they are just being lazy. So in the end I don't go to the discussions anyways cuz they aren't helpful lol.

Overall, I don't think I learned much from this class besides how to cram 10 weeks of material two days before the final. Take it if you want, but I don't really think this is the AI class you would want :)

不要浪費時間修這堂課 人生有更重要的事可以做 :))))

The professor is truly knowledgeable on the theory of machine learning. The first part of the class, regarding the theory and the proof is interesting, where he successfully made the rigorous mathematical proof easy to follow and enhanced our understanding of Machine Learning. The second part is more practical comparing to the first half of the class.

Very interesting course structure, and will be specially helpful to students whose research involves machine learning. However, the course is way too theoretical and math-heavy and the professor makes this very clear in the first lecture. Sometimes, it used to get difficult to follow the lectures, but I guess that is mainly because of the online delivery of instructions. Thankfully, the course textbook is very good and you can study from the book if you missed the lectures. Just one warning - the work load is just way too much. All assignments are to be submitted in LaTex, which takes a lot of your effort. It is almost as good as studying two courses. By the time the quarter ended, I was exhausted with the subject. The only respite is that grading is veryy relaxed and it is easy to score an A+ or A. The professor even drops the worst score from your homework and quiz and does not include it in the final grade. The TAs were very helpful and in general, the discussion sessions were very informative and helpful for the homeworks. Overall, I did learn a lot from this course, but God, I wished the homeworks were not asked to be done on LaTex.

First time teaching so it was a easy class. Gu goes pretty slow and isn’t the most engaging lecturer. Most people chose not to go to class and just put everything on the cheat sheet. Exam questions come straight from his PowerPoint so if you can copy all the information it’s pretty easy to ace. Projects can all be found on GitHub.

This course is definitely among the first courses you would like to take if you major in machine learning. The first half of the course is about the most important theoretical aspects of machine learning, most importantly the approximation-generalization tradeoff. The second half is about typical problems of machine learning like regression, classification, ranking, etc.

The course has fantastic slides. They are clear and are pretty much what you will need for the final exam. Prof. Gu is really good at handling questions, so you needn't worry even though the course is math-heavy because prof. Gu will help you through every equation if you have questions.

The homeworks are challenging and take me a lot of tome, but helps a lot in exam preparation. There are quizzes which are quite easy (mainly about basic concepts). The group project looks scary at first, but you are free to choose from a wide range of topics. The final exam (take-home exam) is pretty like the homeworks.