In the case of the fair coin, the probability of each outcome is equal at 0.5 for heads and 0.5 for tails. A higher level book that examines several applications of the kinds of statistical learning techniques, including weather, earthquakes, athletic performance, and more.Great question - I see how that could be confusing. The Signal and the Noise, Silver, 2012.We will focus mostly on supervised and unsupervised learning, but this book is a good introduction to the key ideas in reinforcement learning. Reinforcement Learning: An Introduction, Sutton and Barto, 1998.The book on support vector machines and related kernel methods. Learning with Kernels, Scholkopf and Smola, 2001.A great resource if you want to know the details. A (somewhat intense) book that considers the more theoretical aspects of statistical learning. A Probabilistic Theory of Pattern Recognition, Devroye, Gyorfi, and Lugosi, 1996.I view Murphy to be a slightly more modern update, but for a lot of classic material this is still my go-to resource. A good introduction to machine learning from a Bayesian perspective. Pattern Recognition and Machine Learning, Bishop, 2006.This book was another close second to Hastie et al., but it isn't available online. Another good overview of much of what we will cover in this course. Pattern Classification, Duda, Hart, and Stork, 2000.
A close second to Hastie et al., this book is also great and is an excellent resource with some material covering more modern topics such as deep learning. Machine Learning: A Probabilistic Perspective, Murhpy.This book covers most of the material we will be covering in the class and is probably the best overall resource that is freely available on the internet. The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman, 2009.It doesn't cover everything we will talk about, but has a fantastic and very accessible overview of VC theory. Learning from Data, Abu-Mostafa, Magdon-Ismail, and Lin, 2012.
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