Machine Learning Resources
A collection of machine learning resources - books, courses, papers, software... - for students.
Index
Books
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
Chapters
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
Courses
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
Single Lectures
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
PhD Theses
Some useful PhD theses from students around the world:
- Understanding Random Forests: From Theory to Practice - Gilles Louppe, Université de Liège
Papers
A selection of useful papers - mostly reviews and tutorials.
Journal and Magazine Papers
- 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
Conference Papers
StackExchange
Some useful StackExchange questions from several communities.
Cross Validated
- 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?
Stack Overflow
Software
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
Blogs
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:
People
Some professors, researchers and students working in machine learning:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent