Here you can find a selection of my publicly available teaching material. The rest can be found on my Github.
Machine Learning for Biomedical Informatics
Interactive Exploration of Topics in Multivariable Calculus
Introduction to Scientific Computing in iPython
Getting Started with Machine Learning
This is a nonexhaustive list of blog posts and textbooks that students in my courses have found useful in the past.

Best Paper Awards in Computer Science since 1996  A very nice list collected by professor Jeff Huang at Brown.

Seeing Theory: A visual introduction to probability and statistics  Interactive explanations and they are working on a textbook.

Homemade Machine Learning  Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.

Artificial Intelligence — The Revolution Hasn’t Happened Yet  Great article by statistician Michael Jordan about the current state of AI.

Mythbusters: Deep Learning Edition  Nice talk by Sasha Rakhlin about “myths” of Deep Learning. Nice citations.

The Limitations of Deep Learning and The Future of Deep Learning  Great reflection pieces by François Chollet, the author of Keras.

Theories of Deep Learning  Great course taught at Stanford (STATS 385) which seeks to build theoretical frameworks deriving deep networks as consequences. Lecture material and video lectures are available online.

Massive Computational Experiments, Painlessly  Great course taught at Stanford (STATS 285) which provides lectures and assignments online.

Learning From Data Online Course  Delivered by the author of the textbook this shortcourse was based off. Homeworks, and video recorded lectures are available online.

Think Bayes  1st edition by Allen Downey  book, code repository for 1st edition which contains all latex and python code/notebooks accompanying the book. Note: This book can be read online and downloaded for free! There is also a code repository for the 2nd edition, which is ahead of the book.

Think Stats  2nd edition by Allen Downey  book and code repository which contains all latex and python code/notebooks accompanying the book. Note: This book can be read online and downloaded for free!. There is also a smaller code repository for the 1st edition, (see the tutorial file for helpful information on this repo).

Think Python  2nd edition by Allen Downey  book and code repository which contains all latex and python code/notebooks accompanying the book. Note: This book can be read online and downloaded for free!

Bayes for Undergrads Workshop  by Allen Downey  Materials for a workshop on developing undergraduate classes on Bayesian statistics.

Bayesian Seminar Series by Allen Downey  code and slides to a couple of seminars he gave.

A Concrete Introduction to Probability (using Python)  Notebook by Peter Norvig

Probability, Paradox, and the Reasonable Person Principle)  Notebook by Peter Norvig

Counterintuitive Properties of High Dimensional Space  A great explanation of the curse of dimensionality

There’s Plenty of Room in the Corners  A great interactive explanation of the curse of dimensionality

On Expressiveness and Optimization in Deep Learning  Nadav Cohen  The talk we discussed at the end of Lecture 2 (watch the first 20 minutes or so)

Mathematics for Machine Learning  Free textbook published online

Foundations of Data Science: Computational Thinking with Python  Berkeley course on edX

Machine Learning Crash Course with TensorFlow APIs  Google course