My Recommendations to Learn Mathematics for Machine Learning
Here is a compilation of books, videos, and papers to get you started.
I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going.
This is not an exhaustive list but I have carefully curated it based on my experience and observations.
This is a repost of my Twitter thread that you can find here. I will keep updating the list here as I come across more useful resources.
Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
Source: https://mml-book.github.io
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
Pattern Recognition and Machine Learning
by Christopher Bishop
Source: https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning/
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it’s heavily referenced in academia.
The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Note: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,…
Probability Theory: The Logic of Science
by E. T. Jaynes
Source: https://bayes.wustl.edu/etj/prob/book.pdf
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
Multivariate Calculus by Imperial College London
by Dr. Sam Cooper & Dr. David Dye
Video Playlist: https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23
Note: backpropagation is a key algorithm for training deep neural nets that rely on Calculus. Get familiar with concepts like chain rule, Jacobian, gradient descent,.
The Matrix Calculus You Need For Deep Learning
by Terence Parr & Jeremy Howard
Source: https://arxiv.org/abs/1802.01528
Note: In deep learning, you need to understand a bunch of fundamental matrix operations. If you want to dive deep into the math of matrix calculus this is your guide.
Mathematics for Machine Learning — Linear Algebra
by Dr. Sam Cooper & Dr. David Dye
Video Playlist: https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3
Note: a great companion to the previous video lectures. Neural networks perform transformations on data and you need linear algebra to get better intuitions.
Information Theory, Inference and Learning Algorithms
by David J. C. MacKay
Note: When you are applying machine learning you are dealing with information processing which in essence relies on ideas from information theory such as entropy and KL Divergence,…
