Learning Machines

Taught by Patrick Hebron at NYU/ITP, Fall 2017
Previous Editions: Fall 2016, Fall 2015


This half-semester course aims to introduce machine learning, a complex and quickly evolving subject deserving of a far more intensive study. The goal of this course is to open a preliminary investigation of the conceptual and technical workings of a few key machine learning models, their underlying mathematics, their application to real-world problems and their philosophical value in understanding the general phenomena of learning and experience.

Course Syllabus

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Required Text

Anderson, Britt. Computational Neuroscience and Cognitive Modelling: A Student's Introduction to Methods and Procedures. Los Angeles: SAGE, 2014.

Additional Resources

Machine Learning Tutorials and Resources
Machine Learning Courses
Math for Machine Learning
General Interest
Python Installation Resources
Python Resources
Academic Research


Portions of the above course materials have been excerpted from Machine Learning for Designers, a text I published with O’Reilly Media, Inc. in 2016. They have been reprinted on this site with permission from the publisher.