Home > CS-7641 ML
Catergories: Foundational Course, Interactive Intelligence, Machine Learning, Computational Perception and Robotics
Course Details: https://omscs.gatech.edu/cs-7641-machine-learning
Course Review: https://omscentral.com/courses/CS-7641/reviews

Reading Reference

Machine Learning
Artificial Intelligence A Modern Approach (4th Edition)
Hands on Machine Learning with Scikit-Learn, Keras, and TensorFlow


I had attempted this during fall of 2021 with ML4T. It did not go well. It was too much work and too chaotic and to save my grades I ended up withdrawing just before midterms & focussed my energy on ML4T. I expected the next time I took it after ML4T and AI, it would make more sense. Do note that CS 6601 AI is listed as a prerequisite for this course.

This is not a class for the weak of heart. It will eat up a lot of time, energy and motivation. If you do not need this to get your degree, or browsing through courses, it would strongly recommend to stay away from it. At the end, if you have survived, you do learn a lot, but the experience is utterly frustrating. There are a lot of people who enjoy the class, sadly I am not one of them. Some thrive in the chaos, but I do not!

Recommended Prerequisites

As so many others, a good understanding of Python is a must for this class. There are a lot of theoretical concepts to grasp and you will not find time to pick up a new language on the side. Good understanding of linear algebra and probability are good to have. If you are not from a CS background and starting OMSCS, this is not the class to begin with! (If this feels repetitive from my post on AI, its because it is!)

Development Environment

This class offers a lot of flexibility ranging from choosing datasets to choosing frameworks. There are some suggestions like mlrose-hiive and MDP-toolbox, but you are free to choose differently.


The projects are open ended and focuses more on analysis. You can use any library or write code from scratch (not recommended due to time constraints), but the most import part are the graphs, and the accompanying explanations. Be sure to include as many whys as possible:

Some recommendations for choosing dataset:

Some generic report writing tips:

Once again, office hours are crucial. If they are at an inconvenient time, watch the recordings. The projects have a 50% weightage on you final grade.

Supervised Learning

You start with two “interesting” classification datasets and run different supervised methods and compare and contrast their performance. Be sure to choose datasets that show come contrast across different algorithms and among themselves for few of them. Hyper parameter tuning plots and learning curves are a must, along with ample analysis of why they are the way they are.

Randomized Optimization

Here you choose some problem domains (using recommended libraries are helpful here as they come with prebuilt problems), and run different randomized optimization to solve them. Again you’ll need to tune the parameters and note the impact. Be sure to summarize which algorithm is the right fit for which problem and why.

Unsupervised Learning & Dimensionality Reduction

Mix and match of different dimensionality reduction (or feature transformation) with clustering algorithms. You’ll run each individually and then combine them. As always, analysis is the key. How do the different dataset fare with different DR techniques. How does the clustering work without DR and with different DR.

Reinforcement Learning (MDP)

Select 2 MDP (you can use one from the recommended library and one from Open AI Gym), and run it through a model based learning and a model-free learning. Compare and contrast the relative performance and analyze the why. Rewards are the domain knowledge, so be sure to tweak the rewards as you deem necessary.


There are two (non cumulative) exams, which are closed everything. You dod not get even a scratch paper. Both of them have 25% weightage each and the final has the possibility of overriding the midterm if better. The questions are subjective with a mix of True/False (you get points for explanation only), and long answer type. Mathematical derivations are not needed.


This is a very long paper, and supposedly designed as such that it cannot be completed. So ensure to focus your time and energy to questions that you are well aware of. I started with the T/F questions and I would recommend to not do that. They contribute little, and at the beginning you tend to write more than required.


While it was supposed to be easier, and some students agreed, I beg to differ. I felt it was harder, but there was enough time to complete the paper this time. I ended up getting lower for final and hence no grade substitution for me!

Learning outcome and application



Grading is the fun part. Every thing is subjective. The projects are graded by different TAs, and you get varying grades throughout the semester. The requirements are fluid and most of the hard requirements are discussed in Office hours. The subjective exams are also a hit and miss. I felt I fared better in the final, but ended up scoring lower. While this might be a good mock of real life where your efforts and the results are not linearly related, but as a part time student with other responsibilities, I expected a saner and structured approach to the class. If you are over the mean of class overall and also for most of the assignments and exams, you can expect to get an A (no promises though).

ML Grade distribution
Grade Distribution for ML over the years (source)

While the professor is very active in discussion forum and even slack, but half the time you’ll get unhelpful snarky remarks. The head TA and the experienced classmates will make the class bearable.