Penn's Engineering Master's Advisory Board (EMAB) has announced Tutoring Program for master's students in Machine Learning. This program will serve as a resource to help students strengthen their skills in the area. Students can drop in at one of our sessions whenever they need help -- no commitment required and free of charge. If a student is interested in this program, they are encouraged to learn about our program at https://pennemab.weebly.com/tutoring.html.

Here are primers on calculus, vector geometry, LaTeX. In addition, here are IPython notebooks introducing Python (including numpy and pandas), linear algebra, the singular value decomposition (SVD), and probability.

Mathematics Background (Probability, Calculus, Linear Algebra):
• Mathematics for Machine Learning Textbook by Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong, Cambridge University Press, 2020
• Mathematics for Machine Learning Notes by Sebastian Raschka

Probability Resources:
• Probability Review

Linear Algebra Resources:
• 3Blue1Brown's Youtube series on the Essence of Linear Algebra
• Jim Hefferon's textbook
• Gilbert Strang's video lecture course

Python Resources:
• Python tutorial (work at least through Section 5; skip Sections 2, 3.1.3)
• Google Developer Python Tutorial
• NumPy Tutorial
• Numpy Tutorial (Stanford)
• Learn Python on Kaggle
• Python for Data Analysis iPython notebooks
• Python quick reference

Hands-On Machine Learning Through Interactive iPython Notebooks:
• Hands-On Machine Learning
• Dive into Deep Learning
• Neuromatch Academy
• Cornell Applied ML
• Python Machine Learning

Other Useful Textbooks, Courses, and Lecture Notes:
• Reinforcement Learning: An Introduction by Sutton and Barto, MIT Press, 1998
• Machine Learning by Tom Mitchell, McGraw Hill, 1997
• A Course in Machine Learning by Hal Daumé III
• Machine Learning Lecture Notes by Andrew Ng
• Making Friends with Machine Learning: A 6-hour course (ML skipping most of the math) by Cassie Kozyrkov
• Machine Learning for Intelligent Systems by Kilian Weinberger

More advanced treatment of machine learning topics:
• Pattern Recognition and Machine Learning by Bishop, Springer, 2006
• Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, MIT Press, 2021
• The Elements of Statistical Learning 2nd Edition by Hastie, Tibshirani and Friedman, Springer-Verlag, 2008
• Convex Optimization by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press, 2004
• Information Theory, Inference, and Learning Algorithms by David Mackay, Cambridge University Press, 2003
• Deep Learning by Yoshua Bengio, Ian Goodfellow, and Aaron Courville