#223 overall
Linear Algebra: Foundations to Frontiers
University of Texas at Austin
2 recommenders · 3 threads · recommended in 2 years, 2014–2017 · score 1.9
A free, self-paced linear algebra course from UT Austin that teaches matrix theory and computation with an emphasis on the practical algorithms and connections to current research. Lecture notes and videos are available through the ulaff.net site.
Commenters point to it as a gentler, approachable route into linear algebra, with lecture notes available free for self study.
Mathematicsbeginnerfreemixed#linear algebra#matrices#numerical linear algebra#van de geijn#self-paced
Recommendations by year
20062026
If you want to learn about this stuff but need a more gentler introduction, check out Robert van de Geijn's MOOC at http://www.ulaff.net (if there is currently no session, just download the lecture notes and use those, they have lecture videos embedded).
Check out Linear Algebra: Foundations to Frontiers (https://www.edx.org/course/linear-algebra-foundations-fronti...), it's being offered self paced by edx, and you can download the lecture notes for free at http://www.ulaff.net. When I took the course two years ago I liked how the lecturer will discuss basic concepts in the lectures but also gives additional material related to state of the art research being done in the field.
There also used to be a course called Coding the Matrix, I'm not sure if it is still being offered online. The lecture notes form a book of the same title, which is available for less than 10$ (Kindle).
Thank you for your advice. In my case it's not "re-learn linear algebra" but "learn linear algebra... after first learning calculus and how to understand/write a proof." :) At 32 I'm not certain if this is a worthwhile way for me to go...
That being said, I haven't given up completely. I'm starting to read "The Haskell Road to Logic, Maths, and Programming" in the hopes of finally being able to grok proofs. At the very least, I feel that learning more math can only help me as a developer.
For others reading this, this edx course on Probability seemed like it was really good, until my lack of maths background caught up to me: https://www.edx.org/course/mitx/mitx-6-041x-introduction-pro... For Linear Algebra, check out http://www.ulaff.net/
Do what feels good for you :)
Ng is a fine place to start, you get some pretty quick wins, doing MNIST from first principles within a month or two. You just need to know or get comfortable with matrix multiplication. It strikes a reasonable balance between being rigorous and approachable for a committed student at an undergrad level.
Principles of Statistical Learning is easier https://lagunita.stanford.edu/courses/HumanitiesandScience/S...
LAFF linear algebra is just starting http://www.ulaff.net/
Hinton's Neural Networks is offered in the fall https://www.coursera.org/learn/neural-networks
For my money, I wouldn't do something like Practical Machine Learning in R, because I think you'll learn more R than machine learning. I wouldn't do the Udacity TensorFlow course because I think it assumes a lot of stuff you would learn in Ng's class ... I think Ng is a fine place to start.