M2 Second semester, courses
Inference, learning & big data
This course provides an introduction to the theory and the practice of inference and learning from data. I discuss the different kinds of learning from data: supervised, unsupervised and reinforcement learning. As a simple example of supervised learning I discuss the « perceptrons » and « support vector machines ». I also discuss in a lesser detail multilayer neural networks and « deep learning ». The problem of data clustering is addressed in detail and used to illustrate unsupervised learning. I discuss applications from computer revised learning. I discuss applications from computer science (hand writing recognition, matrix completion), biology (inverse Ising models for protein folding), and associative memory (Hopfield model). The course includes 3 tutorials to implement the discussed algorithms.
- Information Theory, Inference, and Learning Algorithms, D.J.C. MacKay, Cambridge University Press.
- The Elements of Statistical Learning: Data Mining, Inference and Prediction,T. Hastie, R. Tibshirani and J. Friedman, Springer.
- Statistical Mechanics of Learning, A. Engel and C. Van den Broeck, Cornell University Press.
- Data Classification Algorithms and Applications, C. C. Aggarwal, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series.
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