CSE/STAT 416, Summer 2019: Resources

Online Reading References

All resources on this page are optional and are here for you if you need extra help or want to dive deeper into the material. Most of the materials listed here are available for free and are online, while a few physical textbooks are also referenced. Resources that start with [square brackets] are referenced on the calendar.

Learning Python

• [Schafer] Java 2 Python 3: A guide to learning Python by comparing it to Java - Hunter Schafer
  • Note: Only useful if you know Java.
• Learn Python in Y Minutes
• Very comprehensive Python cheat sheet (has a lot of advanced material, some of which we will eventually learn)

Machine Learning Texts

• [ESL] The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie, Robert Tibshirani, Jerome Friedman
• [Duame] A Course on Machine Learning - Hal Duame
• [Murphy] Machine Learning: A Probabilistic Probabilistic Perspective - Kevin Murphy
• [Bishop] Pattern Recogntion and Machine Learning - Chris Bishop
• [FoML] Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Data Science Libraries

• [VanderPlas] Python for Data Science - Jake VanderPlas
• [Berkely] Principles and Techniques of Data Science - UC Berkely Data 100 Course

Course Infrastructure

• Gradescope Tutorial