Bibliographie

Livres sur le machine learning

• The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman

• Python for Data Analysis, Wes McKinney

• Building Machine Learning Systems with Python, Willi Richert, Luis Pedro Coelho

• Learning scikit-learn: Machine Learning in Python, Raúl Garreta, Guillermo Moncecchi

• Modeling Creativity: Case Studies in Python, Tom De Smedt

• Deep Learning, Yoshua Bengio, Ian Goodfellow and Aaron Courville

• Artificial Intelligence: A Modern Approach, Stuart Russell, Peter Norvig

• Speech and Language Processing, Daniel Jurafsky and James H. Martin, voir aussi Draft chapters in progress

• The Hundred Page Machine Learning, Andriy Burkov (sur github : ` <aburkov/theMLbook>`_)

• Critical Mass: How One Thing Leads to Another, Philip Ball

Livres sur les algorithmes

• Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein

• The Algorithm Design Manual, Steven S. Skiena

• Competitive Programming, Steven Halim

Livres sur la programmation

• High Performance Python, 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, Numba, Cython PyPy, ou CPython, je recommande d’y jeter un coup d’oeil d’abord.

Liens sur la programmation

• Python Scientific Lecture Notes

• Introduction to matplotlib

• Introduction to Data Processing with Python

• Quelques idées de livres : Python for Data Scientists

• Ultimate guide for Data Exploration in Python using NumPy, Matplotlib and Pandas

• Don’t use Hadoop - your data isn’t that big

• Prédire les épidémies avec Wikipedia, Le Monde

• FastML (blog sur le machine learning)

• Mathematical optimization: finding minima of functions

• you can take the derivative of a regular expression?! (2016/06)

• How to trick a neural network into thinking a panda is a vulture (2016/06)

• Matrix Factorization: A Simple Tutorial and Implementation in Python (2016/06)

• Top-down learning path: Machine Learning for Software Engineers

Tutoriels

• PyData Seattle 2015 Scikit-learn Tutorial (2015/12)

• Pythonic Perambulations (2015/12)

• Python Scripts posted on Kaggle (2016/02)

• Pandas cookbook (2016/06)

• Machine Learning & Deep Learning Tutorials (2016/06) : lien vers une liste assez longue de tutoriels, on y trouve aussi des cheat sheets comme Probability Cheatsheet

MOOC

• Machine Learning par Andrew Y. Ng (les chapitres X et XI de la semaine 6 aborde la construction d’un système de machine learning).

• Coursera Machine Learning

• Coursera Machine Algorithm

• CSE373 - Analysis of Algorithms - 2007 SBU

• CS109 Data Science (Harvard) (la liste des vidéos disponibles est en bas)

Autres cours, notebooks

• Arthur Charpentier, lectures (français)

• CS109 Data Science (Harvard) - TD - Talks

• Notes and assignments for Stanford CS class CS231n Convolutional Neural Networks for Visual Recognition

• Advanced Statistical Computing, Chris Fonnesbeck (Vanderbilt University)

• CS 188: Artificial Intelligence (Berkeley)

• IAPR: Teaching materials for machine learning

• machine learning et musique Audio Content Analysis, teachings

• ogrisel’s notebook (2016/04)

• L’apprentissage profond, Yann LeCun au Collège de France (2016/06)

• MA 2823 Foundations of Machine Learning (Fall 2016) (2016/10)

Articles d’auteurs très connus

• Latent Dirichlet Allocation, David M. Blei, Andrew Y. Ng, Michael I. Jordan

• Analysis of a Random Forests Model, Gerard Biau

• Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression, Francis Bach

• Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising, Léon Bottou, Jonas Peter et Al.

• Tutorial on Practical Prediction Theory for Classification, John Langford

• Sparse Online Learning via Truncated Gradient, John Langford, Lihong Li, Tong Zhang

• Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference, Moontae Lee, David Mimno

• ABC model choice via random forests, Pierre Pudlo, Jean-Michel Marin, Arnaud Estoup, Jean-Marie Cornuet, Mathieu Gautier, Christian P. Robert

• Mondrian Forests: Efficient Online Random Forests, Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

• Stochastic Gradient Tricks

• SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases, Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani

• Learning from Partial Labels, Timothee Cour, Benjamin Sapp, Ben Taskar

• Word Alignment via Quadratic Assignment, Simon Lacoste-Julien, Ben Taskar, Dan Klein, Michael I. Jordan

• Contextual Bandit Learning with Predictable Rewards, Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford, Robert E. Schapire

• Learning from Logged Implicit Exploration Data, Alex Strehl, John Langford, Lihong LiSham, M. Kakade

• The Metropolis-Hastings algorithm, Christian P. Robert

• From RankNet to LambdaRank to LambdaMART: An Overview, Christopher J.C. Burges

Compétition de code

• Google Hash Code, a lieu chaque année en deux tours, le second tour a lieu chez Google à Paris.

• Google Code Jam

• TopCoder

• UVa Online Judge

• Le problème des huit reines

• Projet Euler

Compétition de machine learning

• datascience.net

• Kaggle

• ImageNet

• SQuAD

Sources d’articles scientifiques

• ShortScience.org

• Journal of Machine Learning Research

Pour finir, Choosing the right estimator :

Librairies

• Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification

• Python extensions to do machine learning

• Related Projects (of machine learning) (2016/03)

• Awesome Machine Learning

• Chaque paragraphe recense des librairies connues sur le sujet.

Vidéos

• Beyond Bag of Words A Practitioner’s Guide to Advanced NLP

• Building Continuous Learning Systems