Quanquan Gu
Department of Computer Science
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3.8
Overall Rating
Based on 13 Users
Easiness 3.6 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 3.5 / 5 How clear the professor is, 1 being extremely unclear and 5 being very clear.
Workload 4.0 / 5 How light the workload is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 3.7 / 5 How helpful the professor is, 1 being not helpful at all and 5 being extremely helpful.

TOP TAGS

  • Uses Slides

GRADE DISTRIBUTIONS

28.9%
24.1%
19.3%
14.5%
9.6%
4.8%
0.0%
A+
A
A-
B+
B
B-
C+
C
C-
D+
D
D-
F

Grade distributions are collected using data from the UCLA Registrar’s Office.

29.5%
24.6%
19.7%
14.7%
9.8%
4.9%
0.0%
A+
A
A-
B+
B
B-
C+
C
C-
D+
D
D-
F

Grade distributions are collected using data from the UCLA Registrar’s Office.

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Reviews (11)

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Quarter: Winter 2019
Grade: A
April 4, 2022

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 this intro level AI course was designed years ago and ofc it is a little outdated, but the content can hardly be changed unless the department decides to.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.

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Quarter: Winter 2022
Grade: A
March 28, 2022

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.

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Quarter: Winter 2020
Grade: A
April 3, 2020

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.

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Quarter: Winter 2020
Grade: A
March 18, 2020

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!

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Quarter: Winter 2021
Grade: A
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
April 1, 2021

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.

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Quarter: Winter 2022
Grade: A
March 28, 2022

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 :))))

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Quarter: Winter 2022
Grade: A
April 4, 2022

The professor did a decent job explaining the concepts of conventional AI and showing the applications of these algorithms. The first part of this class is taught with lisp, an oldish programming language, which could be replaced by some modern languages. The second part is more about logic and the professor is excellent at extending this to modern AI tasks. There are attendance quizzes helping us review. Midterm and final are easy, and the professor is helpful making accommodations. Discussion should be better to host in person, but TAs are nice explaining the requirements of the homework.

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Quarter: Winter 2021
Grade: A-
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
April 6, 2021

Professor is not a great lecturer and the slides/lectures are pretty boring. Somehow despite being surface-level info and overviews, they are still too "in it" to be interesting. In depth examples appear on slides in place of actually helpful overall rules.
That being said, the class is still pretty easy if you read the textbook, google the terms that come up in the assignments, and browse the slides. I didn't even watch most of the lectures.

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Quarter: Winter 2021
Grade: A
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
April 3, 2021

I absolutely loved this class and felt that I learned a lot from it. I was really excited about the topics covered in this course, like constraint-satisfaction problems, all the different types of search algorithms, first-order logic, and Bayesian nets. This course really teaches you many basic and useful techniques in classical AI.

Professor Gu is truly amazing. He made the lectures interesting and gave a lot of good insights and examples on the topics. During the lecture, he always took time to slow down and made sure that all questions were answered. He also gave extra office hours when the material got harder. He is very helpful, intelligent, and truly cares about his students.

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Quarter: Winter 2020
Grade: N/A
April 1, 2020

I stopped going to class around Week 2, because it's just impossible to stay awake with his teaching. Most topics can be learned from the slides, but I struggled with reasoning under uncertainty and after. Homeworks were from past quarters. Midterm was also derived from past exams and is pretty simple if you're comfortable with tracing through searches, backtracking, alpha-beta pruning, and putting the rest of conceptual info on a cheat sheet. Lisp is absolute ass and I hated HW 2 and 3. The conceptual HWs on logic, 5 and 6, also sucked because you had to provide way too much work for your answers. In conclusion, take this class with Gu if you've become accustomed to learning content on your own and then suffering through homeworks alone. Shoutout to corona for saving me from taking the final.

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Quarter: Winter 2019
Grade: A
April 4, 2022

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.

Helpful?

2 0 Please log in to provide feedback.
Quarter: Winter 2022
Grade: A
March 28, 2022

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.

Helpful?

1 0 Please log in to provide feedback.
Quarter: Winter 2020
Grade: A
April 3, 2020

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.

Helpful?

1 0 Please log in to provide feedback.
Quarter: Winter 2020
Grade: A
March 18, 2020

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!

Helpful?

1 0 Please log in to provide feedback.
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
Quarter: Winter 2021
Grade: A
April 1, 2021

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.

Helpful?

1 1 Please log in to provide feedback.
Quarter: Winter 2022
Grade: A
March 28, 2022

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 :))))

Helpful?

1 2 Please log in to provide feedback.
Quarter: Winter 2022
Grade: A
April 4, 2022

The professor did a decent job explaining the concepts of conventional AI and showing the applications of these algorithms. The first part of this class is taught with lisp, an oldish programming language, which could be replaced by some modern languages. The second part is more about logic and the professor is excellent at extending this to modern AI tasks. There are attendance quizzes helping us review. Midterm and final are easy, and the professor is helpful making accommodations. Discussion should be better to host in person, but TAs are nice explaining the requirements of the homework.

Helpful?

0 0 Please log in to provide feedback.
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
Quarter: Winter 2021
Grade: A-
April 6, 2021

Professor is not a great lecturer and the slides/lectures are pretty boring. Somehow despite being surface-level info and overviews, they are still too "in it" to be interesting. In depth examples appear on slides in place of actually helpful overall rules.
That being said, the class is still pretty easy if you read the textbook, google the terms that come up in the assignments, and browse the slides. I didn't even watch most of the lectures.

Helpful?

0 0 Please log in to provide feedback.
COVID-19 This review was submitted during the COVID-19 pandemic. Your experience may vary.
Quarter: Winter 2021
Grade: A
April 3, 2021

I absolutely loved this class and felt that I learned a lot from it. I was really excited about the topics covered in this course, like constraint-satisfaction problems, all the different types of search algorithms, first-order logic, and Bayesian nets. This course really teaches you many basic and useful techniques in classical AI.

Professor Gu is truly amazing. He made the lectures interesting and gave a lot of good insights and examples on the topics. During the lecture, he always took time to slow down and made sure that all questions were answered. He also gave extra office hours when the material got harder. He is very helpful, intelligent, and truly cares about his students.

Helpful?

0 0 Please log in to provide feedback.
Quarter: Winter 2020
Grade: N/A
April 1, 2020

I stopped going to class around Week 2, because it's just impossible to stay awake with his teaching. Most topics can be learned from the slides, but I struggled with reasoning under uncertainty and after. Homeworks were from past quarters. Midterm was also derived from past exams and is pretty simple if you're comfortable with tracing through searches, backtracking, alpha-beta pruning, and putting the rest of conceptual info on a cheat sheet. Lisp is absolute ass and I hated HW 2 and 3. The conceptual HWs on logic, 5 and 6, also sucked because you had to provide way too much work for your answers. In conclusion, take this class with Gu if you've become accustomed to learning content on your own and then suffering through homeworks alone. Shoutout to corona for saving me from taking the final.

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0 0 Please log in to provide feedback.
1 of 2
3.8
Overall Rating
Based on 13 Users
Easiness 3.6 / 5 How easy the class is, 1 being extremely difficult and 5 being easy peasy.
Clarity 3.5 / 5 How clear the professor is, 1 being extremely unclear and 5 being very clear.
Workload 4.0 / 5 How light the workload is, 1 being extremely heavy and 5 being extremely light.
Helpfulness 3.7 / 5 How helpful the professor is, 1 being not helpful at all and 5 being extremely helpful.

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  • Uses Slides
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