Some good books on Machine Learning: Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach): http://www.amazon.com/Machine-Learnin

Some good books on Machine Learning: Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach): http://www.amazon.com/Machine-Learning-Science-Algorithms-Sense/dp/1107422221/ http://www.amazon.com/Machine-Learning-Science-Algorithms-Se... Machine Learning: A Probabilistic Perspective (Murphy): http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ http://www.amazon.com/Machine-Learning-Probabilistic-Perspec... Pattern Recognition and Machine Learning (Bishop): http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ http://www.amazon.com/Pattern-Recognition-Learning-Informati... There are some great resources/books for Bayesian statistics and graphical models. I've listed them in (approximate) order of increasing difficulty/mathematical complexity: Think Bayes (Downey): http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/1449370780/ http://www.amazon.com/Think-Bayes-Allen-B-Downey/dp/14493707... Bayesian Methods for Hackers (Davidson-Pilon et al): https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers https://github.com/CamDavidsonPilon/Probabilistic-Programmin... Doing Bayesian Data Analysis (Kruschke), aka "the puppy book": http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp/0124058884/ http://www.amazon.com/Doing-Bayesian-Data-Analysis-Second/dp... Bayesian Data Analysis (Gellman): http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954/ http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-... Bayesian Reasoning and Machine Learning (Barber): http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Barber/dp/0521518148/ http://www.amazon.com/Bayesian-Reasoning-Machine-Learning-Ba... Probabilistic Graphical Models (Koller et al): https://www.coursera.org/course/pgm https://www.coursera.org/course/pgm http://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193/ http://www.amazon.com/Probabilistic-Graphical-Models-Princip... If you want a more mathematical/statistical take on Machine Learning, then the two books by Hastie/Tibshirani et al are definitely worth a read (plus, they're free to download from the authors' websites!): Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ http://www-bcf.usc.edu/~gareth/ISL/ The Elements of Statistical Learning: http://statweb.stanford.edu/~tibs/ElemStatLearn/ http://statweb.stanford.edu/~tibs/ElemStatLearn/ Obviously there is the whole field of "deep learning" as well! A good place to start is with: http://deeplearning.net/ http://deeplearning.net/