A collection of machine learning resources - books, courses, papers, software... - for students.
The following list contains several books - whenever free online versions are available, this should be noted correspondingly.
- Pattern Recognition and Machine Learning - Christopher M. Bishop
- Machine Learning: A Probabilistic Perspective - Kevin Murphy
- Bayesian Reasoning and Machine Learning - free pdf - David Barber
- Probabilistic Graphical Models: Principles and Techniques - Daphne Koller, Nir Friedman
- The Elements of Statistical Learning: Data Mining, Inference and Prediction - free pdf - Trevor Hastie, Robert Tibshirani, Jerome Friedman
Some isolated chapters interesting for machine learning students:
- Some Notes on Applied Mathematics for Machine Learning - Christopher J. C. Burges, Advanced Lectures on Machine Learning, 2004
Nowadays, every major technical university offers courses on machine learning and related topics (data mining, computer vision, applied statistics, ...):
- Machine Learning - slides - Bastian Leibe, RWTH Aachen
- Prediction: Machine Learning and Statistics - lecture notes - Cynthia Rudin, MIT
- Methods for Applied Statistics: Unsupervised Learning - lecture notes, slides - Lester Mackay, Stanford
- Data Mining - lecture notes, videos* - Jeff Phillips, University of Utah
- Machine Learning - lecture notes - Andrew Ng, Stanford
- Advanced Machine Learning - partial lecture notes - Ryan Adams, Harvard University
- Bayesian Estimation of Time-Varying Systems - lecture notes - Simo Särkkä, Aalto University
- Introduction to Machine Learning (2014, 2013, 2012) - slides - David Sontag, NYU
A list of single lectures/classes - slides or lecture notes - interesting for students in machine learning.
- Kernel Density Estimation - Non Parametric Econometrics, Bruce Hansen, University of Wisconsin Madison
Some useful PhD theses from students around the world:
- Understanding Random Forests: From Theory to Practice - Gilles Louppe, Université de Liège
A selection of useful papers - mostly reviews and tutorials.
- Learning Deep Architectures for AI - Yoshua Bengio, Foundations and Trends in Machine Learning, 2009
- A few useful things to know about machine learning - Pedro Domingos, Communications of the ACM, 2012
- Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning - Antonio Criminisi, Jamie Shotton, Ender Konukoglu, Foundations and Trends in Computer Graphics and Vision, 2012
Some useful StackExchange questions from several communities.
- Cross-validation or bootstrapping to evaluate classification performance?
- Feature selection with random forests?
- Measure of variable importance in random forests?
- Kernel Bandwidth: Scott's vs. Silverman's rules?
- Silverman's rule to calculate the bandwidth in kernel density estimation?
Machine learning software and libraries:
- Sherwood C++ and C# code library for decision forests
- Boykov-Kolmogorov algorithm for max-flow/min-cut
- OpenCV
- Tiny CNN
- Online Random Forests
Some research-oriented blogs related to machine learning. Note that these blogs are not necessarily updated anymore, however, may still contain interesting articles on machine learning.
Personal blogs:
Some professors, researchers and students working in machine learning: