Resources for further learning

IDS:705 Principles of Machine Learning

Python Programming

The following online courses on Coursera are a 5-part series on Python programming by Nick Eubank, Kyle Bradbury, Andrew Hilton, and Genevieve Lipp:

• Python Programming Fundamentals
• Data Science with NumPy, Sets, and Dictionaries
• Pandas for Data Science
• Designing Larger Python Programs for Data Science
• Data Visualization and Modeling in Python

There is also a textbook version of much of this material by Nick Eubank and Kyle Bradbury

Math for machine learning (calculus and linear algebra)

• Mathematics for Machine Learning by Deisenroth, Faisal, and Ong
• Deep Learning; Part I: Applied Math and Machine Learning Basics by Goodfellow, Bengio, and Courville
• The Matrix Calculus You Need For Deep Learning by Parr and Howard
• Dive Into Deep Learning; Appendix: Mathematics for Deep Learning by Weness, Hu, et al.

Introductory books for machine learning

Each of these are used in one or more course reading assignment in this course:

• An Introduction to Statistical Learning with Python, by James, Witten, Hastie, and Tibshirani
• Understanding Deep learning by Simon Prince
• Introduction to Data Mining, by Tan, Steinbach, Karpatne, and Kumar
• Pattern Recognition and Machine Learning, by Bishop
• Deep Learning, by Goodfellow, Bengio, and Courville
• Reinforcement Learning: An Introduction: An Introduction, by Sutton and Barto