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.

C24_PAE: To apply and adapt technical knowledge and practical skills in the field of telecommunication engineering, participating in problem_solving and the development of solutions in a professional environment. KOPT_1: To know and understand in depth advanced technologies in the specific field of engineering and information and communication technologies, which constitute the state of the art in the area of study, including emerging trends and recent developments. KOPT_2: To interpret sources of scientific and technical information in order to deepen the knowledge of a specific area related to engineering and information and communication technologies. SOPT_1: Identify, assess their technical feasibility and apply advanced technological tools, methodologies and solutions used in the field of engineering and information and communication technologies to develop algorithms or systems integrating innovative and cutting_edge technologies. SOPT_2: Apply analytical and design methodologies to solve advanced problems in the field of engineering and information and communication technologies, and evaluate the performance and limitations of different technological approaches, proposing improvements and alternatives COPT_1: To conceive and develop projects that integrate advanced knowledge and provide innovative solutions in the field of engineering and information and communication technologies.

- Document preprocessing and vectorization techniques. - Keyword extraction and automatic document summarization. - Fundamentals of binary classification: Logistic Regression and k-NN. - Document analysis: POS tagging and NER. - Vector representation: Word and Doc embeddings. - Topic modeling. - Multilayer Neural Networks (MLPs). - Deep learning: convolutional networks. - Language processing with Recurrent Neural Networks: RNNs and LSTMs. - Transformers and Language Models (LLMs)

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.