Checking date: 02/06/2022


Course: 2022/2023

Deep learning
(19206)
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)
EPI


Coordinating teacher: MARTÍNEZ OLMOS, PABLO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




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, treatment of temporal signals and text, and the adjustment of probabilistic models for the generation of of artificial data.
Skills and learning outcomes
Description of contents: programme
1. Deep Neural Networks for computer vision 2. Deep Neural Networks for sequential processing: seq2seq, encoder-decoder networks, transformers 3. Deep probabilistic models
Learning activities and methodology
Theoretical practical classes Laboratory practices Tutorials Team work Student individual work 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.