The course provides the fundamental concepts of supervised classification via deep learning methods, with typical examples in NLP.
1. General concepts for supervised classification
2. A first classifier : k-NN
3. Linear and log-linear models
4. Extension to Multi-layer perceptrons
5. Learning as loss minimization
6. vectorial representations
Lab sessions will illustrate the course, introducing in particular: