- Mathematics for Machine Learning
- Dive into deep learning
- Machine Learning: A Probabilistic Perspective by Kevin Murphy
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie Robert Tibshirani & Jerome Friedman
- Pattern Recognition and Machine Learning by C.M. Bishop,
- Machine Learning by Tom Mitchell
- Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville

Lecture notes, course handouts, pointers to relevant papers, and other materials will be available as HTML and PDF documents on Relate and Piazza

**The Scipy Lectures**- Learn Python the hard way
- Python tutorial
- Facts and myths about Python names and values
- CSE workshop training material
- From Python to Numpy (An open-access book on numpy vectorization techniques, Nicolas P. Rougier, 2017)

- Introduction to Python for Science
- Numpy/Scipy documentation
- More in this reddit thread
- An introduction to Numpy and SciPy
- 100 Numpy exercises
- The Numpy MedKit by StÃ©fan van der Walt

- Matrix Cookbook
- Immersive Linear Algebra
- Essence of Linear Algebra (YouTube, by 3Blue1Brown)
- Fast.ai Linear Algebra Course (Fast.ai, notes and video)
- Linear Algebra (YouTube, by MathTheBeautiful)

- Statistics for Hackers by Jake VanderPlas