Checking date: 15/05/2020

Course: 2019/2020

Natural Language Processing
Study: Bachelor in Mobile and Space Communications Engineering (217)

Coordinating teacher: MARTÍNEZ OLMOS, PABLO

Department assigned to the subject: Department of Signal and Communications Theory

Type: Electives
ECTS Credits: 3.0 ECTS


Students are expected to have completed
The students are expected to have basic knowledge of - Calculus - Programming skills - Statistics
Competences and skills that will be acquired and learning results. Further information on this link
- 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.
Description of contents: programme
- Document preprocessing techniques - Topic Modeling - Recurrent Neural Networks - Language Models with RNNs - Sequence to Sequence for Machine Translation - Attention Models
Learning activities and methodology
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.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Basic Bibliography
  • Cristopher Bishop. Pattern Recognition and Machine Learning. Springer. 2006
  • Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press. 2017
  • Steven Bird, Ewan Klein, Edward Loper . Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly. 2009

The course syllabus and the academic weekly planning may change due academic events or other reasons.