- Sept. 8: Introduction to GitHub
- Sept. 11: Confidence Intervals Handout
- Sept. 18: Collaborative Filtering Exercise
- Oct. 25: Naive Bayes Exercise and Data
- Mitchell, Tom M. Machine Learning. WCB/McGraw-Hill, Boston, MA, 1997.
- Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. The Elements of Statistical Learning. Springer-Verlang, 2009. Website: http://web.stanford.edu/~hastie/ElemStatLearn/
- James, Gareth, Witten, Daniela, Hastie, Trevor, and Tibshirani, Robert. An Introduction to Statistical Learning (with Applications in R). Springer-Verlang, 2013. Website: http://www-bcf.usc.edu/~gareth/ISL/
- Talking Machines: Human Conversations about Machine Learning [link]
NB: these videos are posted to inspire thought and do not necessarily represent the views or opinions of the professor. Each video is copyright TED and are the original material of the presenters.
TED Talks on Machine Learning: A collection of thought provoking talks by machine learning researchers and practitioners at TED conferences.
Jeremy Howard: "The Wonderful and Terrifying Implications of Computers that can Learn" from TEDxBrussels (Dec 2014) [Machine Learning, Applications, Society]
Tom Gruber: "How AI can Enhance Our Memory, Work, and Social Lives" from TED2017 (April 2017) [Machine Learning, Applications]