References

Optional books with relevant material:

• 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

Python Help

• 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)

Numpy Help

• 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 Stéfan van der Walt

Linear Algebra

• 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

• Statistics for Hackers by Jake VanderPlas