Georgia Tech OMSCS Courses - Awards
List of courses I did and how I rate them.
I ended up doing twelve OMSCS courses (Minimum requirement for the degree is ten courses). And I wanted to do more — but then, I also have a wife and a life.
This blog lists down and rates the courses I did for OMSCS. Everyone is different and everyone can have their own opinions — so my list may be completely different from a list of a fellow student — but that is alright. Also, I can review the courses I took — so my awards have a limited scope. If you have a list too — add it to the response section.
There is little objectivity here. Please refer to https://omscentral.com/ before deciding whether the course is right for you. For biased, subjective opinions read on.
Machine Learning for Trading — The Best Course Overall
Meticulously designed course!! Professor Balch, I am a fan. The course is structured into three sections — Python, basics of Trading, and introduction to Machine Learning. I learned Python, Pandas, Numpy, Stock Trading, Options Trading, Machine Learning basics — and then applied all of these skills in a final well-thought project. The best part —this course was not hectic at all. It requires lots of skills to deliver a course which teaches you a lot and is not tedious about it. Well done!!
Honorable Mentions: Reinforcement Learning, Intro to Graduate Algorithms
Intro to Graduate Algorithms — The Least tedious
It is the most controversial entry in this awards list!! But I loved the course and liked the approach of zero “housekeeping” time. There are no tedious assignments, no lengthy boring lessons, no tricky questions — the only thing that matters is your understanding of the subject — and if you follow lectures and practice https://www.leetcode.com, you should do good. Lecture videos are too the point. You don’t need to struggle with lengthy assignments.
The only reason that this course is not number one — the exam format made it very risky — there are six unseen questions. If you fail one you get a B. If you fail two, you get a C.
Honorable Mentions: Machine Learning for Trading
Reinforcement Learning— The best lecture videos
Professor Charles Isbell and Michael Littman are knowledgeable, enthusiastic, and funny. Videos are lengthy and could have been succinct — but I don’t mind spending time watching these two guys talk.
And the course has an awesome Open AI project — you got to land a helicopter — fun overall.
Honorable Mentions: Intro to Graduate Algorithms, Machine Learning (The same professor combo)
Artificial Intelligence — The best programming course
Six programming assignments — all neck-breaking AI / ML problems. But awesome fun and a lot to learn. I learned some cool python programming tricks. Its a very tedious course — you are going to spend lots of time programming, you are going to love it.
Honorable Mentions: (Distant honorable mentions) Machine Learning for Trading, Reinforcement Learning, Data Visual Analysis.
Machine Learning — The Time Sink
Survey course on all Machine Learning techniques. Awesome videos (Professor Charles Isbell and Michael Littman). But the assignments are time killers — you need to write code, choose a dataset, and write a report explaining your ML insights.
Professors explained key aspects for many ML algorithms: Decision Trees, Regression & Classification, Neural Networks, Instance-Based Learning, Ensemble B&B, SVMs, Bayesian Learning, Randomized Optimizations, MDPs, Reinforcement Learning, and Game theory.
Honorable Mentions: Artificial Intelligence
Knowledge-Based Artificial Intelligence — The Writers Choice
They make you write. You have to write really lengthy reports — it’s lots of theory — and some programming — three programming projects overall.
I believe I wrote more than 60 pages overall — I could write a book in four semesters like this. If you like theory and like to write about it — you will love this course.
Honorable Mentions: Machine Learning
Education Technology — For the self-driven
This course is what you make of it. It is modeled after a Ph.D. program. This course centers around creating a mini-thesis project that has something to do with educational technology. So, you define what you need to do.
Honorable Mentions: None
Computational Photography — The most Photogenic Course
This course was fun — we played with pictures, computationally. We even created a Portfolio. We also learned a few Computer Vision tricks as well — edge and feature detection. Professor Essa is enthusiastic and it shows. Few assignments were tedious — and if you have read other reviews — I don’t like tedious.
Honorable Mentions: None
Data & Visual Analysis — The best course in R programming
This course won this award because it was the only contender. The course was not bad. I learned a lot about data visualization techniques. The lectures were good and the assignments were interesting. The best thing I learned from this course is the R language.
Honorable Mentions: None
Intro to Info Security & Computer Networks— The best additional course
At times, you will need to do two courses in one semester.
Intro to Info Security is an ideal class to pair with a more complex course. The course has a low workload — but did discuss some cool ideas on cryptography, hashing, encryption, and security protocols.
Computer Network is not tedious either. It conveys theory concepts concisely and has decent assignments/exams.
Honorable Mentions: None
Computability, Complexity & Algorithms — The Boo-Boo Course
Lectures didn’t make sense, and it looked like exam questions were designed for a different course altogether. It is only befitting that this course was discontinued and replaced with “Intro to Graduate Algorithms”.
Honorable Mentions: None
