.. _l-td2A-biblio:

=============
Bibliographie
=============

.. contents::
    :local:

Livres sur le machine learning
==============================

* `The Elements of Statistical Learning <http://statweb.stanford.edu/~tibs/ElemStatLearn/>`_, Trevor Hastie, Robert Tibshirani, Jerome Friedman
* `Python for Data Analysis <http://shop.oreilly.com/product/0636920023784.do>`_, Wes McKinney
* `Building Machine Learning Systems with Python
  <https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python>`_,
  Willi Richert, Luis Pedro Coelho
* `Learning scikit-learn: Machine Learning in Python
  <https://www.packtpub.com/big-data-and-business-intelligence/learning-scikit-learn-machine-learning-python>`_,
  Raúl Garreta, Guillermo Moncecchi
* `Modeling Creativity: Case Studies in Python <http://arxiv.org/abs/1410.0281>`_, Tom De Smedt
* `Deep Learning <http://www-labs.iro.umontreal.ca/~bengioy/dlbook/>`_,
  Yoshua Bengio, Ian Goodfellow and Aaron Courville
* `Artificial Intelligence: A Modern Approach <http://aima.cs.berkeley.edu/>`_,
  Stuart Russell, Peter Norvig
* `Speech and Language Processing <http://www.cs.colorado.edu/~martin/slp.html>`_,  Daniel Jurafsky and James H. Martin,
  voir aussi `Draft chapters in progress <https://web.stanford.edu/~jurafsky/slp3/>`_
* `The Hundred Page Machine Learning <http://themlbook.com/>`_, Andriy Burkov
  (sur github : ` <aburkov/theMLbook>`_)

* `Critical Mass: How One Thing Leads to Another
  <http://www.philipball.co.uk/index.php?option=com_content&view=article&id=15:critical-mass-how-one-thing-leads-to-another&catid=3:books&Itemid=4>`_,
  Philip Ball

Livres sur les algorithmes
==========================

* `Introduction to Algorithms <http://mitpress.mit.edu/books/introduction-algorithms>`_,
  Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein
* `The Algorithm Design Manual <http://www.algorist.com/>`_, Steven S. Skiena
* `Competitive Programming <http://www.comp.nus.edu.sg/~stevenha/myteaching/competitive_programming/cp1.pdf>`_, Steven Halim

Livres sur la programmation
===========================

* `High Performance Python <http://shop.oreilly.com/product/0636920028963.do>`_, Micha Gorelick, Ian Ozsvald.
   Le livre est très bien conçu et les exemples sont très clairs. Si vous souhaitez accélérer un programme Python
   en utilisant le multithreading, `OpenMP <http://openmp.org/wp/>`_,
   `Numba <http://numba.pydata.org/>`_, `Cython <http://cython.org/>`_
   `PyPy <http://cython.org/>`_, ou `CPython <https://en.wikipedia.org/wiki/CPython>`_,
   je recommande d'y jeter un coup d'oeil d'abord.

Liens sur la programmation
==========================

* `Python Scientific Lecture Notes <http://scipy-lectures.github.io/>`_
* `Introduction to matplotlib <https://scipy-lectures.github.io/intro/matplotlib/matplotlib.html>`_
* `Introduction to Data Processing with Python <http://opentechschool.github.io/python-data-intro/>`_
* Quelques idées de livres : `Python for Data Scientists <https://www.packtpub.com/books/content/python-data-scientists>`_
* `Ultimate guide for Data Exploration in Python using NumPy, Matplotlib and Pandas <http://www.analyticsvidhya.com/blog/2015/04/comprehensive-guide-data-exploration-sas-using-python-numpy-scipy-matplotlib-pandas/#One>`_
* `Don't use Hadoop - your data isn't that big <http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html>`_
* `Prédire les épidémies avec Wikipedia <http://www.lemonde.fr/sante/article/2014/11/13/predire-les-epidemies-avec-wikipedia_4523461_1651302.html>`_, Le Monde
* `FastML <http://fastml.com/>`_  (blog sur le machine learning)
* `Mathematical optimization: finding minima of functions <http://scipy-lectures.github.io/advanced/mathematical_optimization/index.html>`_
* `you can take the derivative of a regular expression?! <http://jvns.ca/blog/2016/04/25/how-regular-expressions-go-fast/>`_ *(2016/06)*
* `How to trick a neural network into thinking a panda is a vulture <https://codewords.recurse.com/issues/five/why-do-neural-networks-think-a-panda-is-a-vulture>`_ *(2016/06)*
* `Matrix Factorization: A Simple Tutorial and Implementation in Python <http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/>`_ *(2016/06)*
* `Top-down learning path: Machine Learning for Software Engineers <https://github.com/ZuzooVn/machine-learning-for-software-engineers>`_

Tutoriels
=========

* `PyData Seattle 2015 Scikit-learn Tutorial <https://github.com/jakevdp/sklearn_pydata2015>`_ *(2015/12)*
* `Pythonic Perambulations <https://jakevdp.github.io/>`_ *(2015/12)*
* `Python Scripts posted on Kaggle <https://www.kaggle.com/scripts?language=Python>`_ *(2016/02)*
* `Pandas cookbook <https://github.com/jvns/pandas-cookbook>`_ *(2016/06)*
* `Machine Learning & Deep Learning Tutorials <https://github.com/ujjwalkarn/Machine-Learning-Tutorials>`_ *(2016/06)* :
  lien vers une liste assez longue de tutoriels, on y trouve aussi des *cheat sheets* comme
  `Probability Cheatsheet <http://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf>`_

MOOC
====

* `Machine Learning par Andrew Y. Ng <https://www.class-central.com/mooc/835/coursera-machine-learning>`_
  (les chapitres X et XI de la semaine 6 aborde la construction d'un système de machine learning).
* `Coursera Machine Learning <https://www.coursera.org/course/ml>`_
* `Coursera Machine Algorithm <https://www.coursera.org/course/algo>`_
* `CSE373 - Analysis of Algorithms - 2007 SBU <https://www.youtube.com/playlist?list=PL5F43156F3F22C349>`_
* `CS109 Data Science (Harvard) <http://cs109.github.io/2014/>`_ (la liste des vidéos disponibles est en bas)

Autres cours, notebooks
=======================

* `Arthur Charpentier, lectures <https://freakonometrics.github.io/videos/>`_ (français)
* `CS109 Data Science (Harvard) <http://cs109.github.io/2014/>`_ -
  `TD <https://github.com/cs109/content>`_ -
  `Talks <http://cm.dce.harvard.edu/2015/01/14328/publicationListing.shtml>`_
* `Notes and assignments for Stanford CS class CS231n <https://github.com/cs231n/cs231n.github.io>`_
  `Convolutional Neural Networks for Visual Recognition <http://vision.stanford.edu/teaching/cs231n/>`_
* `Advanced Statistical Computing, Chris Fonnesbeck (Vanderbilt University) <http://nbviewer.jupyter.org/github/fonnesbeck/Bios366/tree/master/notebooks/>`_
* `CS 188: Artificial Intelligence (Berkeley) <http://inst.eecs.berkeley.edu/~cs188/fa10/lectures.html>`_
* `IAPR: Teaching materials for machine learning <http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mlteach.htm>`_
* machine learning et musique `Audio Content Analysis, teachings <http://www.audiocontentanalysis.org/teaching/>`_
* `ogrisel's notebook <https://github.com/ogrisel/notebooks>`_ (2016/04)
* `L'apprentissage profond <https://www.college-de-france.fr/site/yann-lecun/course-2015-2016.htm>`_, Yann LeCun au Collège de France *(2016/06)*
* `MA 2823 Foundations of Machine Learning (Fall 2016) <http://cazencott.info/index.php/pages/MA-2823-Foundations-of-Machine-Learning-%28Fall-2016%29>`_ *(2016/10)*

Articles d'auteurs très connus
==============================

* `Latent Dirichlet Allocation <http://ai.stanford.edu/~ang/papers/jair03-lda.pdf>`_, David M. Blei, Andrew Y. Ng, Michael I. Jordan
* `Analysis of a Random Forests Model <http://www.jmlr.org/papers/volume13/biau12a/biau12a.pdf>`_, Gerard Biau
* `Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression <http://jmlr.csail.mit.edu/papers/volume15/bach14a/bach14a.pdf>`_, Francis Bach
* `Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising <http://jmlr.csail.mit.edu/papers/volume14/bottou13a/bottou13a.pdf>`_, Léon Bottou, Jonas Peter et Al.
* `Tutorial on Practical Prediction Theory for Classification <http://www.jmlr.org/papers/volume6/langford05a/langford05a.pdf>`_, John Langford
* `Sparse Online Learning via Truncated Gradient <http://jmlr.org/papers/volume10/langford09a/langford09a.pdf>`_, John Langford, Lihong Li, Tong Zhang
* `Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference <http://mimno.infosci.cornell.edu/papers/EMNLP2014138.pdf>`_, Moontae Lee, David Mimno
* `ABC model choice via random forests <http://arxiv.org/abs/1406.6288>`_, Pierre Pudlo, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet, Mathieu Gautier, Christian P. Robert
* `Mondrian Forests: Efficient Online Random Forests <http://arxiv.org/pdf/1406.2673v1.pdf>`_, Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
* `Stochastic Gradient Tricks <http://leon.bottou.org/papers/bottou-tricks-2012>`_
* `SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases <http://arxiv.org/abs/1207.4525>`_, Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani
* `Learning from Partial Labels <http://www.seas.upenn.edu/~taskar/pubs/partial_labels_jmlr11.pdf>`_, Timothee Cour, Benjamin Sapp, Ben Taskar
* `Word Alignment via Quadratic Assignment <http://www.seas.upenn.edu/~taskar/pubs/naacl06_qap.pdf>`_, Simon Lacoste-Julien, Ben Taskar, Dan Klein, Michael I. Jordan
* `Contextual Bandit Learning with Predictable Rewards <http://arxiv.org/abs/1202.1334>`_, Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire
* `Learning from Logged Implicit Exploration Data <http://papers.nips.cc/paper/3977-learning-from-logged-implicit-exploration-data>`_, Alex Strehl, John Langford, Lihong LiSham, M. Kakade
* `The Metropolis-Hastings algorithm <http://arxiv.org/abs/1504.01896>`_, Christian P. Robert
* `From RankNet to LambdaRank to LambdaMART: An Overview <http://research.microsoft.com/pubs/132652/MSR-TR-2010-82.pdf>`_, Christopher J.C. Burges

Compétition de code
===================

* `Google Hash Code <https://hashcode.withgoogle.com/>`_, a lieu chaque année en deux tours, le second tour a lieu chez Google à Paris.
* `Google Code Jam <https://code.google.com/codejam>`_
* `TopCoder <http://www.topcoder.com/>`_
* `UVa Online Judge <http://uva.onlinejudge.org/>`_
* `Le problème des huit reines <http://zanotti.univ-tln.fr/algo/REINES.html>`_
* `Projet Euler <https://projecteuler.net/>`_

Compétition de machine learning
===============================

* `datascience.net <https://www.datascience.net/fr/challenge>`_
* `Kaggle <https://www.kaggle.com/competitions>`_
* `ImageNet <http://www.image-net.org/>`_
* `SQuAD <https://rajpurkar.github.io/SQuAD-explorer/>`_

Sources d'articles scientifiques
================================

* `ShortScience.org  <http://www.shortscience.org/>`_
* `Journal of Machine Learning Research <http://www.jmlr.org/>`_

Pour finir, `Choosing the right estimator <http://scikit-learn.org/stable/tutorial/machine_learning_map/>`_ :

.. image:: http://scikit-learn.org/stable/_static/ml_map.png
    :width: 500

Librairies
==========

* `Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification <https://github.com/szilard/benchm-ml>`_
* `Python extensions to do machine learning <http://www.xavierdupre.fr/blog/2013-09-15_nojs.html>`_
* `Related Projects (of machine learning) <http://scikit-learn.org/stable/related_projects.html>`_ (2016/03)
* `Awesome Machine Learning <https://github.com/josephmisiti/awesome-machine-learning#python>`_
* Chaque paragraphe recense des librairies connues sur le sujet.

Vidéos
======

* `Beyond Bag of Words A Practitioner's Guide to Advanced NLP <https://www.youtube.com/watch?v=YWzFxRZPEyU>`_
* `Building Continuous Learning Systems <https://www.youtube.com/watch?v=VtBvmrmMJaI>`_