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.

Portal > Artificial Intelligence > Machine Learning

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Bishop, Christopher M. “Mixture Models and EM” and “Continuous Latent Variables” in Pattern Recognition and Machine Learning, Springer 2007.

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

Hastie, T., Tibshirani, R., and J. Friedman. “The Elements of Statistical Learning: Data Mining, Inference and Prediction.” Springer, 2001.

Hastie, T, Tibshirani, R, and J. Friedman. “The Elements of Statistical Learning: Data Mining, Inference and Prediction.” Springer, 2001. Sec 8.7, 10.1 — 10.11 Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease (2018): https://doi.org/10.48550/arXiv.1504.07676

Murphy, K. “Markov chain Monte Carlo (MCMC) inference” and “Clustering” in Machine Learning: A Probabilistic Perspective, MIT, 2012. Ch 24 &25

Mehryar, Mohri, Rostamizadeh, Afshin, and Ameet Talwalkar. “Introduction” in Foundations of machine learning. MIT press, 2018.

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

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

Barber, David. “Bayesian Reasoning and Machine Learning.” Cambridge University Press, 2012

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