# Machine Learning course

The dates of the course are:

- 24/09 P320
- 01/10 A101
- 15/10 A101
- 22/10 A101
- 05/11 A101
- 12/11 A101
- 03/12 (exam) A007

Here is the syllabus of a course on Machine Learning that Iām teaching in 2018-2019:

- Machine learning introduction (for a printable version )
- linear models: regression, classification
- notion of loss, risk, empirical risk, Bayes risk
- perceptron, maximum margin
- bias-variance tradeoff, overfitting
- evaluation: cross-validation, accuracy, ROC, F1, hyper-parameters, confidence intervals

- Probabilistic models (for a printable version )
- discriminative vs. generative
- directed, undirected graphical models
- maximum likelihood, EM, bayesian inference

- Deep neural networks
- convex optimization: line search, steepest, (Newton, BFGS, conjugate,) SGD
- feed-forward networks: backpropagation, computation graph
- convolutional networks
- recurrent networks (RNN, LSTM, GRU): vanishing gradient
- attention and memory models
- variational autoencoders

- Exercices can be found here

The slides and material will be added on this site.

Written on July 9, 2018