The students are expected to have basic knowledge of - Calculus - Programming skills - Statistics

- Know the basic techniques of text pre-processing. - Use software tools for pre-processing text. - Know the techniques of topic modeling. - Use topic modeling software tools in corpus of documents. - Use topic models for information retrieval in corpus of documents. - Learn how to train models of semantic representation in a vector space. - Learn to train language models using recursive neural networks. - Know basic translation structures based on recursive neural networks. - Use optimization tools to build language models with recursive neural networks.

- Document preprocessing techniques - Topic Modeling - Recurrent Neural Networks - Language Models with RNNs - Sequence to Sequence for Machine Translation - Attention Models

Lectures (1.5 ECTS): Contents: basic theory and methods of natural language processing systems. Methodology: classical lecture with use of slides, videos and white/blackboard. Lab projects (1.5 ECTS): Contents: implementation of algorithms in for the simulation and assessment of natural language processing systems. Methodology: use of numerical software packages (matlab/octave) to study the performance of algorithms and systems.