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