Recently I’ve started to see a lot of questions regarding to the “mathematics tutorials or supplementary materials for Machine Learning and AI” in the online discussions with the emergence of Stanford’s online AI and machine learning courses. As with the internet crowd, I’m going to participate these courses as well and I’ve always found the resources provided in this post as being useful for revising my math knowledge. Hence I thought that those might be useful for other folks as well. Probably a having look at only one resource in each category will be more than enough for an introductory level ML/AI course (you can skip some categories which might have less significance for an introductory level ML and AI course, e.g.: skip diff eqs, numerical analysis and study more to the probability and stats):
Linear Algebra
- D. Barber’s Matrices Notebook – http://www.ucl.ac.uk/Mathematics/geomath/level2/mat/MHma.html
- Edinburgh’s Learning for Data course’s Linear Algebra inclined Mathematics Supplementary document for ML – http://www.inf.ed.ac.uk/teaching/courses/lfd/lfd_supp_maths2005.pdf
- A basic Linear Algebra Tutorial - http://tutorial.math.lamar.edu/Classes/LinAlg/LinAlg.aspx
- Notes on Numeric Linear Algebra – http://gbenthien.net/NumLinAlg.pdf
Calculus
- MIT OCW Calculus Revisited – A great short course for revision of Calculus knowledge- http://ocw.mit.edu/resources/res-18-006-calculus-revisited-fall-2010/
Multivariable Calculus
- Calculus 2 Notes – http://tutorial.math.lamar.edu/Classes/CalcII/CalcII.aspx
Probability and Statistics
- Probability Notebook: http://www.ucl.ac.uk/Mathematics/geomath/level2/prob/MHpb.html
- Probability Part in McKay’s “Information Theory, Inference, and Learning Algorithms” Book – http://wol.ra.phy.cam.ac.uk/mackay/itprnn/book.html
- Probability Theory Review For Machine Learning - http://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf
- Probability and Statistics Review for Machine Learning - http://cs.nyu.edu/~roweis/notes/mlss05.pdf
Differential Equations
- Interactive Mathematics, Introduction to Differential Equations: http://www.intmath.com/differential-equations/des-intro.php
Numerical Analysis
- A numerical Analysis Tutorial (requires java for simulations): http://math.uh.edu/~caboussat/Java/NumericalAnalysis/english/support/
Intro ML
- Edinburgh’s Introductory Applied Machine Learning Course’s Video – http://groups.inf.ed.ac.uk/vision/VIDEO/2010/iaml.htm
- Stanford OpenClassRoom Machine learning – Andrew Ng’s course- http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning
- Edinburgh Uni’s Learning from Data Lecture Notes – http://www.inf.ed.ac.uk/teaching/courses/lfd/lfdlectures.html
Matlab
- Introductory Matlab Notes – http://www.inf.ed.ac.uk/teaching/courses/lfd/matlab2005.pdf
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