Resources

IDS:705 Principles of Machine Learning

Introductory books for machine learning

• 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

Math for machine learning

• 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.

Python Programming

The following online courses on Coursera are a 5-part series on Python programming:

• 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