En visitant notre site, vous acceptez notre politique de confidentialité concernant les cookies, les statistiques de suivi, etc.

Rejoignez-nous au Binding Hook Live le 27 octobre au Underbelly Boulevard Soho à Londres.
Rejoignez-nous au Binding Hook Live
Demander une invitation

Machine Learning

This module explores machine learning, focusing on algorithms and models that enable computers to learn from and make predictions or decisions based on data. It covers supervised, unsupervised, and reinforcement learning techniques, along with practical applications in areas such as image recognition, natural language processing, and predictive analytics.

Portail > Artificial Intelligence > Machine Learning

Bâtisseur de programmes d'études

Voir le programme d'études

Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Barber, David. Bayesian Reasoning and Machine Learning. 1st ed. Cambridge University Press, 2012.

https://doi.org/10.1017/CBO9780511804779

MacKay, David J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.

Bishop, Christopher M. Pattern Recognition and Machine Learning. Information Science and Statistics. New York: Springer, 2006.

Mohri, Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar. “Introduction.” In Foundations of Machine Learning, 504. The MIT Press, 2018.

https://mitpress.mit.edu/9780262039406/foundations-of-machine-learning/

Murphy, Kevin P. “Chapter 24: Markov Chain Monte Carlo (MCMC) Inference” and “Chapter 25: Clustering.” In Machine Learning A Probabilistic Perspective. London, England: The MIT Press, 2012.

Wyner, Abraham J., Matthew Olson, Justin Bleich, and David Mease. “Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers.” arXiv, April 29, 2017.

http://arxiv.org/abs/1504.07676

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer Series in Statistics. New York, NY: Springer New York, 2009.

https://doi.org/10.1007/978-0-387-84858-7

Wasserman, Larry. All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics. New York, NY: Springer New York, 2004.

https://doi.org/10.1007/978-0-387-21736-9

Merci de vous être inscrit à notre lettre d'information !

Thank you! RSVP received for Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Merci d'avoir posé votre candidature ! Nous vous contacterons.

Apply for: Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning Series. Cambridge, Massachusetts: The MIT Press, 2018.

Chargement...

Chargement...