Machine Learning Foundations - BMIF 6315 - 2018

This repository is for Machine Learning Foundations lectures given as part of the BMIF 6315 course in 2018 at Vanderbilt University.

Students can submit anonymous questions/comments/concerts to me by filling out this Google form

Due to Github space constraints lectures can be found at this Google Drive link

Note: You can run all notebooks in this repository interactively by:

• using the Binder hosting service by clicking here:
• uploading all files in this repository to Google's new Colaboratory Notebook Service

Resources

• 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 short-course 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