CIS 4190/5190: Applied Machine Learning (Spring 2023)
syllabus      schedule      files      resources

EMAB tutoring

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


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