Checking date: 26/04/2023

Course: 2023/2024

Natural Language Processing
Bachelor in Sound and Image Engineering (Plan: 441 - Estudio: 214)

Coordinating teacher: MARTÍNEZ OLMOS, PABLO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
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.
Skills and learning outcomes
Description of contents: programme
- Document preprocessing techniques - Automatic summarization. Clustering techniques - Text embeddings - Topic Modeling - Neural Networks and Recurrent Neural Networks - Text processing with neural networks
Learning activities and methodology
All sessions will be theoretical / practical, in which each session introduces a theoretical aspect and is developed using specific software libraries. It is important to highlight that these classes will require initiative and personal and group work on the part of the student (there will be concepts that they will have to study personally based on some indications, particular cases they will have to develop, etc.) These practices, on the one hand, allow the student apply the theoretical knowledge acquired to try practical solutions, so that they can consolidate and critically analyze such knowledge. ECTS credits include in all cases the corresponding part of personal or team work by the student.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
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 may change due academic events or other reasons.