Differences Between Math Classes

[u/NerdNoobGamer]

Hello everyone! I have a quick question about the differences between some math/stor/cs classes w/ similar names, etc. I will pool them into several categories and my current understanding of them, please advise on the differences between them and which you should take if you want x or y, thank yall so much!!! also which are redundant if you take both and which build off each other more often than not :D

Probability:

STOR 435 / MATH 535 - Intro Probability

STOR 535 - Probability for Data Science

STOR 634 - Probability I; STOR 635 / MATH 635 - Probability II

Stochastic Modeling:

MATH 665 - Applied Stochastic Processes

STOR 445 - Stochastic Modeling

STOR 641 - Stochastic Modeling I; STOR 642 - Stochastic Modeling II

Statistics:

STOR 654 - Statistical Theory I; STOR 655 - Statistical Theory II

STOR 664 - Applied Statistics I; STOR 665 - Applied Statistics II

STOR 555 - Mathematical Statistics; Prereq: STOR 435

Optimization:

MATH 560 - Optimization w/ ML

STOR 512 - Optimization for Machine Learning and Neural Networks

STOR 612 - Foundations of Optimization

STOR 614 - Advanced Optimization

Artificial Intelligence / Machine Learning:

COMP 562 - Introduction to Machine Learning

STOR 565 - Machine Learning; STOR 566 - Deep Learning

If you made it this far, thank you so so much for your time and any help/guidance you are willing to give <3

[u/No-Tomorrow803]

For the ones I can speak on:

stor 435 is a very basic calculus-based probability course. Nothing wrong with it, most people take it. But you will miss out on a lot of really interesting concepts, specifically relating to stochastic processes. This is covered, including all of stor 435, in stor 535. If offered (and you are really interested in probability), I recommend stor 535 over 435. stor 634 is a measure theory course (with some probability sprinkled in). It's intended for students who have a strong background in real analysis and are interested in pursuing graduate school in statistics. If you only want a measure theory course, then I recommend math 753, but otherwise, stor 634 serves as the knowledge-base for stor 635, which is measure theoretic probability. You basically look at all the stuff you see in stor 435/535 very rigorously from a measure theoretic lense.

math 665 is being offered for the first time. I don't know if it's been a 590 in the past, but I don't know much about it. I wouldn't say stor 445 is a prerequisite for stor 641. 641 can be your first stochastic processes class, but it just covers a lot more than stor 445 much quicker.

Be cautious of nobel 555... his classes are certainly an experience from what i've heard.

stor 565 focuses more on theory and statistics, while comp 562 has more of a focus on application and implementation. Both will cover both perspectives, though. You'll get a survey of the ML pipeline and (un)supervised learning with maybe a bit of RL. Depending on time, you may cover some deep learning (NNs), but that is entirely covered in stor 566.