Useful references


  • Bayesian Reasoning and Machine Learning by David Barber [pdf]
  • The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman [html] [pdf]
  • Information Theory, Inference, and Learning Algorithms by David J.C. MacKay [html] [pdf]
  • Convex Optimization by Stephen Boyd and Lieven Vandenberghe [pdf] [html]
  • Natural Image Statistics by Aapo Hyvärinen, Jarmo Hurri and Patrik O. Hoyer [html] [pdf]
  • The Quest for Artificial Intelligence - A History of Ideas and Achievements by Nils J. Nilsson [html] [pdf]
  • Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams [html] [pdf]
  • Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze [html] [pdf]


  • Journal of Machine Learning Research [html] [papers]
  • Neural Computation [html]


  • Neural Information Processing Systems (NIPS) [html] [papers]
  • International Conference on Machine Learning (ICML) [html]
  • International Conference on Artificial Intelligence and Statistics (AISTATS) [html]
  • Uncertainty in Artificial Intelligence (UAI) [html] [papers]


  • Learning deep architectures for AI (literature review on deep learning) by Yoshua Bengio [pdf]
  • The Matrix Cookbook by Kaare Brandt Petersen and Michael Syskind Pedersen [pdf]
  • Structured Learning and Prediction in Computer Vision (tutorial on structured output prediction applied to computer vision) by Sebastian Nowozin and Christoph Lampert [pdf]

Code and datasets

  • MLPython: my group's machine learing research library (see the documentation here);
  • Theano: a Python library for easily defining, computing, optimizing and symbolicallly manipulating mathematical expressions, on the CPU or GPU;
  • CUDAMat: a Python library supporting the computation of common matrix operations on the GPU;
  • GNumPy: a NumPy-like library for easily manipulating matrices on the GPU;
  • a website dedicated to deep learning, that references many datasets and libraries useful in deep learning research;
  • LIBSVM datasets: a list of datasets for machine learning research, all in the LIBSVM format.