This course had potential but fell rather flat. Professor Kuo knows her stuff but is not a very dynamic lecturer. I found it hard to stay engaged throughout the whole time, particularly since sometimes it felt like the lecture began with super easy content and ended with stuff over my head. There was an attendance policy where she would take attendance on random days and you had to be there for at least five of the days she took attendance (this was annoying, hopefully it will change in future semesters). The five homeworks were a mix of written questions and coding. They weren't too difficult but took some time because of the so-so lecture quality. The take-home midterm exam was pretty hard, mainly because our homeworks had not been graded and we had no other practice problems. Thankfully there was not a final exam. The final project was a group project where you had to do something related to AI, code, and social good. It was pretty easy, especially if you're group doesn't wait til the last minute. The course content was a good survey of classic AI methods, modern ML/NN/LLM stuff, AI applications, and AI ethics. I'd probably recommend taking AI with a different professor but I would also say it's worth going to a few lectures because Professor Kuo could improve as she continues to teach. #tCFS24