Checking date: 31/05/2023

Course: 2023/2024

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

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 - Numerical optimization
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.
Skills and learning outcomes
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
Learning activities and methodology
AF1 Theoretical class AF2 Practical classes AF3 Theoretical and practical classes AF4 Laboratory practices AF5 Tutorials AF6 Group work AF7 Individual student work AF8 Partial and final exams
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
  • Kevin Murphy. Machine Learning A Probabilistic Perspective. MIT Press. 2012

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