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.

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.

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.