Materials

Campus Links

Thanks Professor Winikus!

• General Info (Spr22)
• Health and Wellbeing
• Generic Useful Links

Intro stuff for class

I will cover content from some of these in the first two weeks of lecture, but these materials are handy if you want to brush up on the fundamentals.

This website also has a ton of phenomenal resources for learning python, probability, and statistics.

Probability and Stats

• Seeing Theory
• Khan Academy open AP Stats Course
• CMU’s ML course, math primer
• Probability Cheatsheet
• Introduction to Probability Textbook

Linear Algebra

• The matrix calculus you need for deep learning
• Three Blue One Brown Course on Linear Algebra
• The matrix cookbook

Python programming

• The official python3 tutorial
• Sebastian Raschka’s notebook on intro to scientific computing
• Minitorch, to help understand deep learning tools we will use later in the semester
• Data Science Handbook
• DLab @ Berkeley’s Computational Social Science

Youtube

Two channels I have really enjoyed while preparing materials for this class:

• The aforementioned 3Blue1Brown
• StatQuest

Books

There is no required course textbook. However, I will at times suggest readings from Hal Daumé III Course in Machine Learning Book.

And, some other (free online) books that you may find useful, and/or that we may refer to at some point in the class (oriented, given my expertise, to probabilistic/statistical perspectives):

• Bayesian Data Analysis Third edition
• Machine Learning: A Probabilistic Perspective
• Mathematics for Machine Learning
• Advanced Data Analysis from an Elementary Point of View
• Statistical Learning with Sparsity
• Castella and Berger - Statistical Inference
• Intro to Statistical Learning with R
• Pattern Recognition and Machine Learning (Bishop)

Materials from other classes you might find useful

I found inspiration for our corse from these courses, so you might too!

• Stanford CS221: AI (Autumn 2019)
• Sebastian Raschka’s Introduction to Machine Learning
• Cornell’s Machine Learning for Intelligent Systems
• Introduction to Machine Learning (CMU 10-301)
• Dr. Srihari’s version of this course
• Dr. Varun Chandola’s version of this course
• Introduction to Probability Textbook
• Bayesian Methods for Hackers
• Jordan Boyd-Graber’s Advanced Data Analysis
• Stanford Computer Vision course