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Course: 2025/2026

Deep learning

(19206)

Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

EPI

Coordinating teacher: LANCHO SERRANO, ALEJANDRO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives

ECTS Credits: 3.0 ECTS

Course: 1º

Semester: 1º

Requirements (Subjects that are assumed to be known)

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

Objectives

The fundamental objective of this subject is for the student to know and learn to use learning schemes based on advanced neural networks, with special emphasis on computer vision applications and treatment of audio signals, and the adjustment of probabilistic models for the generation of artificial data.

Learning Outcomes

Link to document

Description of contents: programme

1. Probabilistic modeling with deep networks: VAEs 2. Probabilistic modeling with deep networks: GANs 3. Implicit representation models 4. Segmentation and object detection with deep networks. 5. Deep voice and audio processing 6. 1. Probabilistic modeling with deep networks: Diffusion Models

Learning activities and methodology

Training Activities: AF1 Theoretical classes (using digital presentations) AF2 & AF4 Practical programming classes in the laboratory AF5 Tutoring hours AF6 Group work for solving projects related to deep learning AF8 Midterm and final exams Methodology: MD1 Practical classes MD3 Practical classesatorio MD3 Laboratory practices MD5 Group work Individual exams and tutoring hours

Assessment System

% end-of-term-examination/test 30
% of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment

The continuous evaluation will consist of partial exams, carrying out practices and programming projects and presentation of works.

SE 2 Individual or group work carried out during the course

SE 3 final exam

A final exam will evaluate the student's global knowledge.

The extraordinary evaluation will consist of a final exam.

Basic Bibliography

Bishop, Christopher Michael and Bishop, Hugh. . Deep Learning - Foundations and Concepts. 1 Edited by Springer Cham. 2023
Cristopher Bishop. Pattern Recognition and Machine Learning. Springer. 2006
Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press. 2017
Kevin Murphy. Machine Learning A Probabilistic Perspective. MIT Press. 2012

The course syllabus may change due academic events or other reasons.