#243 overall
18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
MIT OpenCourseWare
2 recommenders · 2 threads · recommended in 2 years, 2020–2022 · score 1.8
MIT OpenCourseWare course taught by Gilbert Strang covering matrix methods used in data analysis, signal processing, and machine learning, including factorizations such as SVD and related techniques for working with data. It is presented as a follow-up to his linear algebra course.
Commenters point to it as Strang's follow-up to his linear algebra course, useful for understanding the math behind machine learning papers.
Mathematicsintermediatefreevideo lectures#linear algebra#matrix factorization#svd#data analysis#strang
Recommendations by year
20062026
You can not really appreciate the beauty of machine learning without the maths behind it. I don't know what your maths background is but if I had to recommend, a good understandig of Linear Algebra makes all the difference when you read ML papers. I would storngly recommend Gilbert Strang's two courses on Linear Algebra [1] and Matrix Methods[2].
Everything else that mostly cares about application is easy to find on the internet (see the r/MachineLearning subreddit).
[1] https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra... [2] https://ocw.mit.edu/courses/mathematics/18-065-matrix-method...
You could take Strang’s follow-up course on learning from data.
https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-an...