Professor
Kai-Wei Chang
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Most Helpful Review
Fall 2022 - Prof. Chang was a good teacher. Slides were very informative, but also a bit overwhelming, especially towards the end of the quarter. Some of the math formulas and derivations for loss functions could get quite complicated and confusing. However, he was a very kind professor, and clearly cared about our learning. He was always willing to stop for questions. Assignments were very easy, but also interesting. There were 6 online 24-hour quizzes, essentially a free 20% grade boost and good review for exams, and 3 python notebook coding assignments. Lectures were mostly conceptual, so these 3 HWs were crucial to understanding the applications of what we learned, as well as a good introduction to sklearn. The midterm was easy, timed and on gradescope, with a mean of 90. The final was much harder, as the concepts at the end of the course were a bit more complex, with harder math as well. The mean was around a 70. However, the rest of the class was pretty easy, and the final was only 30% of the grade, so it's not the end of the world if you stay on top of everything else. Overall, the class was very beneficial to me. I'm very interested in machine learning, and received a great introduction to many concepts, as well as some basic applications. Prof. Chang was very fair, kind, and a pretty good lecturer. I highly recommend this as an elective if you're at all interested in AI/ML.
Fall 2022 - Prof. Chang was a good teacher. Slides were very informative, but also a bit overwhelming, especially towards the end of the quarter. Some of the math formulas and derivations for loss functions could get quite complicated and confusing. However, he was a very kind professor, and clearly cared about our learning. He was always willing to stop for questions. Assignments were very easy, but also interesting. There were 6 online 24-hour quizzes, essentially a free 20% grade boost and good review for exams, and 3 python notebook coding assignments. Lectures were mostly conceptual, so these 3 HWs were crucial to understanding the applications of what we learned, as well as a good introduction to sklearn. The midterm was easy, timed and on gradescope, with a mean of 90. The final was much harder, as the concepts at the end of the course were a bit more complex, with harder math as well. The mean was around a 70. However, the rest of the class was pretty easy, and the final was only 30% of the grade, so it's not the end of the world if you stay on top of everything else. Overall, the class was very beneficial to me. I'm very interested in machine learning, and received a great introduction to many concepts, as well as some basic applications. Prof. Chang was very fair, kind, and a pretty good lecturer. I highly recommend this as an elective if you're at all interested in AI/ML.
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Most Helpful Review
Spring 2022 - This class is an introduction to NLP and covers tasks such as part-of-speech tagging, word representation, syntactic parsing, semantic parsing, co-reference resolution, machine translation and more. The models and algorithms used for these tasks are a mixture of classical ones (e.g Hidden Markov Models) and modern ones (e.g Transformer neural nets), where the class focuses more on the latter. Generally, I am very happy with Prof Chang's delivery of this material. The lectures are well-prepared and interactive and are updated regularly to include new concepts, interesting papers, etc. I especially like the quality of the lecture slides, which are almost good enough to learn from on entirely their own. One issue I had with the class is that it is fairly work-intensive. Here is the list of assignments in the class: -Weekly quizzes (5 in total) -1 midterm group project -1 paper group presentation -1 final group project -1 final exam -Various peer reviews While there are quite a few, I did like the hands-on nature of these assignments. We could implement a range of different approaches for each project and even had the opportunity to peer-review other students' work. I found the latter especially useful as it gives you a better way to compare and learn than only receiving a grade. Overall I can really recommend this class to someone interested in NLP. Its material is current and the instructors genuinely want to help you learn about the field.
Spring 2022 - This class is an introduction to NLP and covers tasks such as part-of-speech tagging, word representation, syntactic parsing, semantic parsing, co-reference resolution, machine translation and more. The models and algorithms used for these tasks are a mixture of classical ones (e.g Hidden Markov Models) and modern ones (e.g Transformer neural nets), where the class focuses more on the latter. Generally, I am very happy with Prof Chang's delivery of this material. The lectures are well-prepared and interactive and are updated regularly to include new concepts, interesting papers, etc. I especially like the quality of the lecture slides, which are almost good enough to learn from on entirely their own. One issue I had with the class is that it is fairly work-intensive. Here is the list of assignments in the class: -Weekly quizzes (5 in total) -1 midterm group project -1 paper group presentation -1 final group project -1 final exam -Various peer reviews While there are quite a few, I did like the hands-on nature of these assignments. We could implement a range of different approaches for each project and even had the opportunity to peer-review other students' work. I found the latter especially useful as it gives you a better way to compare and learn than only receiving a grade. Overall I can really recommend this class to someone interested in NLP. Its material is current and the instructors genuinely want to help you learn about the field.
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