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.