Aprendizaje Automatizado

Libros

• Pattern Recognition and Machine Learning by Christopher M. Bishop [pdf]
• Machine Learning: a Probabilistic Perspective by Kevin Murphy [webpage]
• Bayesian Reasoning and Machine Learning by David Barber [webpage]
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman [webpage]
• Introduction to Machine Learning by Alex Smola [pdf]
• Probabilistic Models in the Study of Language by Roger Levy [pdf]
• Bayesian Artificial Intelligence by Kevin B. Korb and Ann E. Nicholson [pdf]
• Neural Networks and Deep Learning by Michael Nielsen [webpage]

Artículos

• Tom M. Mitchel. The Discipline of Machine Learning [pdf]
• Thomas G. Dietterich. Machine Learning [pdf]
• Alan Hájek. Interpretations of Probability. The Stanford Encyclopedia of Philosophy [link]
• Andrew McCallum and Kamal Nigam. A Comparison of Event Models for Naive Bayes Text Classification [pdf]
• Andrew Ng and Michael Jordan. On Discriminative vs Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes [pdf]
• Daphne Koller, Nir Friedman, Lise Getoor and Ben Taskar. Graphical Models in a Nutshell [pdf]
• Payam Refaeilzadeh, Lei Tang, and Huan Liu. Cross-Validation [pdf]
• Léon Bottou. Online Learning and Stochastic Approximations [pdf]
• Sean Borman. The Expectation Maximization Algorithm: A short tutorial [pdf]
• Asa Ben-Hur and Jason Weston. A User’s Guide to Support Vector Machines [pdf]

Software

• General
• Scikit-learn (Python)
• MLlib (Java/Scala/Python)
• Mlpack (C++)
• Dlib (C++)
• Máquina de vectores de soporte
• SVMLight(C)
• LibSVM(C)
• Modelado probabilístico
• Edward (Python)
• Pyro (Python)
• PyMC (Python)
• PGMPy (Python)
• Pomogranate (Python)
• Redes neuronales
• Tensorflow (C++/Python)
• Pytorch (Python)
• Caffe 2 (C++/Python)
• Keras (Python)

Bases de datos

• Kaggle
• Repositorio de UCI
• KDnuggets Datasets
• Colección de Sebastian Raschka
• datos.gob.mx
• The Big Bad NLP Database
• VisualData
• Google Dataset Search
• Amazon Datasets
• Microsoft Rsearch Open Data
• Awesome Public Datasets

Pláticas

• Bayesian or Frequentist, Which Are You? by Michael I. Jordan [videos]
• Introduction to Bayesian Inference by Christopher M. Bishop [videos]
• Graphical Models and Variational Methods by Christopher M. Bishop [videos]
• Probabilistic Graphical Models by Sam Roweis [videos]
• Graphical Models by Zoubin Ghahramani [videos]
• Graphical Models, Variational Methods, and Message-Passing by Martin J. Wainwright [videos]
• Statistical Learning Theory by John Shawe-Taylor [videos]
• Kernel Methods and Support Vector Machines by John Shawe-Taylor [videos]
• Introduction to Support Vector Machines by Colin Campbell [videos]
• Boosting by Rob Schapire [videos]
• Bayesian models of human inductive learning by Joshua B. Tenenbaum [video]
• Cognitive science for machine learning 3: Models and theories in cognitive science by Tom Griffiths [videos]
• Machine Learning and Cognitive Science by Joshua B. Tenenbaum [videos]

Otros cursos

• Machine Learning by Andrew Ng [webpage]
• Introduction to Machine Learning by Ryan Adams [webpage]
• Learning from data by Yaser Abu-Mostafa [webpage]
• Probabilistic Graphical Models by Daphne Koller [webpage]
• Large Scale Machine Learning by Ruslan Salakhutdinov [webpage]

Congresos y revistas

• Neural Information Processing Systems (NIPS) [webpage]
• International Conference on Machine Learning (ICML) [webpage]
• International Conference on Artificial Intelligence and Statistics (AISTATS) [webpage]
• Uncertainty in Artificial Intelligence (UAI) [webpage]
• Knowledge Discovery and Data Mining (KDD) [webpage]
• International Journal of Machine Learning Research [webpage]
• arXiv.org cs.LG [webpage]
• arXiv.org stat.ML [webpage]