An Exhaustive List of Course Lectures

This post comprises of a list of lectures that I intend to watch at some point in order to mainly just learn more about these domains. They seem like some interesting areas to explore and some are just plain fun!

Note: This list is just for personal reference; you may use it if you like, but it is by no means an exhaustive list.
That said, I have put in a fair bit of research into comparing prerequisites and course information across MIT, Stanford and other colleges whilst compiling this list that should provide a fair overview, to an undergraduate student interested in exploring Machine Learning and Computer Vision, of the courses that relate to the applications of these practices in Genetics and Genomics (yes, they're disparate branches of Biology).

It's just a list of courses that I'm interested in, oriented around ML and CV.


6.006J - Introduction to Algorithms [UG] prerequisite for 6.046J
6.046J - Design and Analysis [UG] prerequisite for 6.84J
6.84J - Advanced Algorithms [G]

Mathematics and Statistics:

18.02 - Multivariable Calculus (II) [UG] prerequisite for 6.041F
18.06 - Linear Algebra [UG] prerequisite for CS229
18.05 - Introduction to Probability and Statistics [UG] prerequisite for CS229
6.041F - Probabilistic Systems Analysis and Applied Probability [UG/G]
6.262 - Discrete Stochastic Processes [G] prerequisite for 10-715 (CMU)

Machine Learning:

10-401, 10-601, 10-701, and 10-715 are all introductory courses but 10-715 and 10-701 are intended for graduate students with a strong mathematical background.

CS229 - Machine Learning [UG]

10-701 - Introduction to Machine Learning [UG/G] prerequisite for 10-702, 10-715
10-702 - Statistical Machine Learning [G]
10-715 - Advanced Introduction to Machine Learning [G]


7.012 - Introduction to Biology [UG] prerequisite for 7.91J, 8.591J
7.91J - Foundations of Computational and Systems Biology [UG/G]

Computer Vision:

CS231N - Convolutional Neural Networks for Visual Recognition [UG]
CAP5415 - Computer Vision [UG]

Optional Courses:

8.591J - Systems Biology [UG/G]
6.034 - Artificial Intelligence [UG]
6.042J/18.062J - Mathematics for Computer Science [UG]

18.085 - Computational Science and Engineering (I) [G]
18.086 - Mathematical Methods for Engineers (II) [G]

18.01 - Single Variable Calculus (I) [UG]

CMU Electives for ML Students