Clearly someone is bitter and leaving negative reviews on all Professor Peng's classes. She is great and I would highly recommend taking her class

I liked this class as an introduction to NLP, but I don't think it went too deep into any topics. The first homework was presenting on a NLP paper and peer reviewing, which I thought was interesting. The second homework was a bit more bland, but I think Professor Peng said she is going to change it after student feedback. Project is also very doable in terms of workload since there's only two homeworks. Exams were really fair and doable as well.

The professor really seems to care about learning and student feedback, so I can only imagine that this class will get better and better as more iterations are offered!

One of the highlights of the course was its strong emphasis on the transition from teaching to research. Professor Peng did an excellent job integrating peer reviews and in-depth analysis of academic papers, which deepened our understanding of NLP topics. The lectures were clear and structured, and the exams were fair.

I think the name of this class should be "Introduction to NLP research". For someone who have background in the NLP/AI research, the content covered is pretty basic. Assignment 1, which is paper reading, is pretty natural to a person accustomed to research. Assignment 2, which I agree is a bit of a chore, probably could be streamlined with API calls to ChatGPT API. Maybe that's what research in NLP is like? I don't know. I feel like the comments about technical details are not doing justice to the professor. I think this class is detailed enough as a research oriented class. (Even for undergrad classes like 146 or 145, the professors are not covering code implementation in class. Implementation is practiced in the projects.) Since the assignments are designed to be research focused, student don't really have the chance to practice implementation of algorithms.
On the other hand, as a research oriented class, I feel that there is too few material that comes from current ongoing research. The current form of this class is somewhere in the middle of the two worlds(content oriented/research oriented), which misses the merit of both worlds.
Therefore, I think the professor need to readjust the focus of the class. Advertise the class as a research oriented class. Put more focus on current research. Leave the fundamental content/implementation project to 162. Make 162 a pre-requisite.

This course is clearly in development, this was the first time Professor Peng was teaching the class. She is very open to feedback and it seems like she will change most of the things I did not enjoy about the class (one of the assignments was very long and largely busy work, but she said she would not be using this in the future).

I really enjoyed the focus on peer reviews and reading/analyzing academic papers on the topics of NLP. The lectures were clear and the tests were not too difficult. I am hopeful that future adaptations of this course by Professor Peng will be very enjoyable and intriguing

Prof. Peng leads students in exploring and understanding the mechanisms of NLP algorithms, and I have greatly enjoyed this process.
She genuinely cares about her students and is always open to discussions. Her classes encourage a lot of interaction, and she makes an effort to remember each student's name. I treasure every conversation with Prof. Peng.
A good university should emphasize critical thinking, rather than just coding skills. The insights and interactions shared by an NLP expert like Prof. Peng are incredibly valuable.

Professor Peng does an excellent job at teaching Natural Language Processing (NLP); she provides a quick recap and overview of all the math fundamentals students need in order to understand advanced NLP concepts. The exams in the class test the understanding of NLP tasks, processes, and current research in the field; exams are tough but most students should be able to pass. I attended each lecture and reviewed them again before exams, and I performed well. The projects are doable, I just recommend setting aside time for them. The homework is not overwhelming as some other courses. Overall this is a great class.

I would suggest that the instructors "space" apart assignment deadlines so that they are not due in back-to-back weeks. That was the only thing I thought could be improved.

The professor is nice and helpful, and ensured that everyone had answers to their questions. If people didn't have questions in class, they would have their answers answers sometimes in minutes. I'm not sure if this will be true in future courses, but she did give us many chances to get credit. If there is one feedback, it is that a small set of people dominate this type of participation. This might be because others were too shy, or needed time to process their thoughts.

As other have noted, this course is geared towards future academics, which I personally think is fine. Both homeworks (there are 2 large ones) were based on research instead of practical applications, although you could a practical application in your final project. I think that it's fair to say that the field of currently important NLP and LLMs in general are at most 10 (!) years old, and that there is more to the field than coding transformers, calling openai/LLM APIs, and prompt engineering.

For the quizes (acting as small-midterms), I felt like the questions were deceptively tricky (the averages were high, so could be a skill issue). If you get 3/4 of a choose multiple options question it's a 0 ;(. I have not taken the final or have received the grade for the final project yet, because this feedback contributes to some bonus points for the final (and I'm not trying to do this review waiting for the final grade my b).

As an MSOL student, the professor seems to be good at communicating and is organized for the online section of the class. The assignments were doable, we were definitely given enough time to complete everything. The quizzes seemed hard. Practice quizzes were provided but the real quizzes seemed more conceptual with information that did not seem to be taught in class. The final project is also doable but there isn't much involvement coming from the professors or TAs to see how it's going. There's a lot of extra credit opportunities. Professor seems very nice and knowledgable on the topics in every lecture.

This course is for students who want to focus on research in the future. You should not take it if you are not interested in research. The professor's lectures are clear and full of knowledge. I have only a little knowledge about NLP but can learn a lot from her lectures. However, the quizzes are harder than I expected, although the average score is high. The practice quizzes only cover a small part of the topics that will be covered in the actual quizzes.

The workload is higher than I expected as well, not because the assignments are hard, but because they are time-consuming. The first assignment is to present a paper. Not only do you need to present, but you also need to write a reading note. (I haven't written a required reading note since I was in junior high.) The second assignment mainly focuses on human annotation. We need to compare the results generated by GPT with those annotated by humans. I think both assignments are designed for students to understand the entire research process clearly. They are not helpful at all for students who are going to find jobs unrelated to research.