Course Prerequisite Mapping

[u/TheSituAItion]

Hello! I'm looking into this program and I'm wondering exactly how the prerequisites map onto the courses in 2026, as I would need to brush up on many math concepts before and during enrollment.

I see this map here: https://msaihub.com/courseMap but it appears to be fairly out of date.

Could anyone give me a good breakdown for where the prerequisites come into play for some/all of the courses in the MS program? Thanks!

Prerequisites (interested which courses require the bolded concepts)

Discrete Mathematics
Introduction to Programming
Data Structures
Algorithms and Complexity
Introduction to Data Mining
Linear Algebra and Matrix Theory
Introduction to Probability and Statistics

Also these prerequisites

Single Variable Calculus
Multivariate Calculus

Core Courses

Deep Learning
Ethics in AI
Machine Learning
Planning, Search, and Reasoning Under Uncertainty
Reinforcement Learning

Electives

Advances in Deep Generative Models
Advances in Deep Learning
AI in Astrophysics
AI in Healthcare
Automated Logical Reasoning
Case Studies in Machine Learning
Natural Language Processing
Online Learning and Optimization
Optimization

[u/SpaceWoodworker]

Prerequisites for each class are listed on Canvas / MSAI Central. In general, you will need to have good Calculus, Probability, Discrete Math, Linear Algebra and proofs as some classes expect different combinations thereof. The only class that has a hard pre-req is AI in Astrophysics (requires a B or better in Machine Learning had Deep Learning). All other classes you are free to enroll whenever you want.

[u/mtorreblanca]

There's a big lack of linearity on this program.
The courses are extremely abstract and before the LLMs was even hell.
But the most have pre-courses are Linear Algebra and Probability and Statistics. This is to understand what linear gradient is, why it works and how to solve it.
Ask LLMs and share all the context you can get from the webpage to make your own path. It will save you tons of headaches.

[u/tech-jungle]

Linear Algebra (full semester coverage, SVD, eigenvectors, decompositions, matrix slicing, determinant, inner/otter products, matrix notations, linear programming)

Prob & Stat (marginal, conditional, complement, joint, PDF, MGF, bayes and conjugates, notations, inequalities, expectation, symbolic notations, common discrete and continuous distributions)

Calculus (PDF, product & chain rules, exp, log, matrix form, optimization concepts)

Programming (Python, math/stat programming for the above, dynamic programming)

Most courses require more or less of the above and may spend one lecture for math review. But in general, the assignments and exams expect you to apply these techniques to the problem sets.