The easy reviews are actually CS 7650 OMSCS version of NLP because OMSCentraI doesn't seem to have them (so pls try OMSHub).

ANLP is really a challenging course. Not only you need to learn the nuts and bolts (so please do well in CSE 6040, CDA and one of AI/RL/DL), you are also challenged on timed quizzes and learning how to make GPT.

But you'll be well-rewarded.

Difficulty has been dramatically upped for this course.

You will need to learn how to make ChatGPT from scratch.

This actually makes ANLP one of the most exciting courses OMSA has to offer.

Try it :)

Very easy and elementary NLP course for a graduate program.

This course is good to have on your resume.

It is the sort of course that you can get through with a very easy A, but if you want to learn anything you need to invest a little more time.

The class work is basically setting up neural network structures from pictures in the jupyter notebooks. Any background reading on why you’re doing it is outside the required scope. The lectures are barebones, usually 15 minutes per week.

I would say this course is a lightly guided walk through of some of the very basics of NLP. It’s along the lines of the coursera ML course by Andrew Ng where you’ve learned enough to fill in the blanks for some of the ML topics, but not enough that you would have a good idea of how to go about choosing and training and improving a model on a random set of data.

The main criticism:

1. Needs more comprehensive homework
2. some better lectures possibly about the application and how to work on the now more difficult homework. The course involves application but very little explanation of why for the application itself
3. eliminate the parts about non-NN models as that’s been covered in many previous courses.

Context: I took this class in the summer of 2023, which is the second semester that this class is offered. This class still has some kinks to iron out, such as issues with Gradescope, issues with varied Python code indentations, version compatibility issues in some homeworks. It is very likely that later semesters will not encounter such issues as the instructors will have already fixed these.

I had minor NLP knowledge before taking this class. This is my last class in the program so I am already very comfortable with coding, various machine learning techniques, working with arrays (linear algebra) using numpy and pytorch and troubleshooting any potential issues with developing environments.

In terms of difficulty and amount of time invested into the homeworks, it was easy for me. Each of the 4 homeworks took less than 4 days total for completion. That time includes some hours spent training models on my laptop CPU and cancelling/restarting the job to walk away due to irl interruptions (since I was stubborn and did not want to use Google's Colab).

The homework difficulty progression is like this: 1 -> 2 -> 4 -> 3. Minimum time to completion: 4 hrs. Maximum time to completion: 4 days.

The lectures in this class are very terse however they do provide the necessary citations/resources for students to dig deeper if they are so inclined. No doubt some reviews will say that this class is thin on materials but I would disagree, since there are excess time left from completing the homeworks, I was able to spend them exploring further into the subject topics based on the provided resources.

The reviews for this class will likely be polar, on the one hand there will be lots of reviews on how thin the materials are or how easy it is, on the other hand there will likely be lots of reviews on the difficulty with Gradescope, version incompatibility issues, pytorch, lack of troubleshooting support etc...

Do keep in mind that this is a graduate level course and there is a base level expectation of being able to troubleshoot and solve problems on your own with minimal hand-holding. Additionally, how much one gets out of the class depends on the level of comfort with the aforementioned subjects. If all the time is spent troubleshooting the shape and rotation of a matrix multiplication problem or on basic python stuffs such as list of lists or list of dicts then there will be less time and brain resources devoted into exploring the actual NLP topics.

Therefore it is my opinion that to get the most out of this class, you have to already be comfortable with basic linear algebra, basic ML techniques, numpy (and therefore its big brother pytorch), scikit-learn, and know-how to set up and troubleshoot your python development environments.

Good course that introduces foundational NLP material. Covers methods of prepping text data for modeling as well as modeling techniques & architectures used for NLP problems.

The most difficult part of the class was implementing models using pytorch. I had no experience with pytorch coming into the class but was able to figure it out via online research. Other than that, the class wasn't too difficult. 9 hours/week workload really only applies to weeks when homework was due. Other weeks it was closer to 5 hours.

I thoroughly enjoyed this class. It's a great class to wrap up the OMSA/OMSCS program because it's not too demanding and it applies much of what you learn in other courses. Would recommend for anyone interested in NLP.