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.

Probably shouldn't use a really light marker in a 200+ person lecture hall, but here are my major faults with this class:
Homeworks I heard are completely copied off of a different machine learning course from UIUC. Midterm and Final questions also heavily are influenced from Dan Roth's course.
Overall the course wasn't conceptually hard. However, a lot of the test questions gave almost no partial credit. Eg, if you had the right answer when the question was "Show X concept", almost no partial credit was given; they looked more for proofs than anything else.
Only a two hour final, and the test questions can be written quite poorly / not clearly. If you're a text-learner, this is a great class! If you're a visual learner and want diagrams on your test, expect to be disappointed.
He also doesn't curve his class at all, rather he scales the margins (eg A- at 88% or something similar, and each letter grade following). However, people did well on his midterm (86-ish median), so he made his final alot harder to artificially decrease the grade distro. IMO this would have been fine if he curved the class, but he doesn't.
Also, he doesn't really consider test statistics that much. Everything is straight scale, so your percentiles on each test dont matter.
Overall, good lecturer, however I found him hard to understand in class sometimes, but bruincast helped out a lot (1.5x speed ftw). However his testing and grading schemes could be a lot better. My suggestions are to: make the midterms and finals more consistent in same difficulty, test on more concepts (almost no SVM questions on final iirc), have more diagrams, and have consistent writing on tests. (EG telling students not to ask questions about the test-questions on the final due to unclear language in the final seems wrong IMO. Especially on a test that ends up having a typo.)