Computer Vision

• Computer Vision - Dana Ballard, Chris Brown

• Computer Vision: Algorithms and Applications - Richard Szeliski

• Computer Vision: Models, Learning, and Inference - Simon J.D. Prince

• Programming Computer Vision with Python - Jan Erik Solem

 

Machine Learning

• A Course in Machine Learning

• A First Encounter with Machine Learning

• AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java - George F. Luger, William A Stubblefield

• An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

• Bayesian Reasoning and Machine Learning

• Computer Vision: Algorithms and Applications

• Gaussian Processes for Machine Learning

• Information Theory, Inference, and Learning Algorithms

• Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan (PDF)

• Introduction to Machine Learning - Amnon Shashua

• Learning Deep Architectures for AI

• Machine Learning

• Machine Learning, Neural and Statistical Classification

• Neural Networks and Deep Learning

• Probabilistic Models in the Study of Language (Draft, with R code)

• Programming Computer Vision with Python - Jan Erik Solem

• The LION Way: Machine Learning plus Intelligent Optimization

• The Python Game Book

• Class Notes from Stanford CS229 - Andrew Ng

• A Course in Machine Learning

• A Brief Introduction to Neural Networks

• Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville

• Learn Tensorflow - Jupyter Notebooks

• Machine Learning, Neural and Statistical Classification