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Course: 2024/2025

Neural Networks

(19203)

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 is that the student learns to design decision machines based on neural networks for basic learning problems in tabular and multimedia data, paying special attention to regularization and validation techniques. Likewise, the student will learn to use automatic differentiation software packages for model training and experimental simulation.

Skills and learning outcomes

Link to document

Description of contents: programme

1. Neural Networks and Backpropagation 2. Regularization and explainability 3. Architectures for high-dimensional correlated data: images, time series and graphs

Learning activities and methodology

MD1 Practical classes MD3 Practical classesatorio MD3 Laboratory practices MD5 Group work Individual exams and tutoring hours

Assessment System

% end-of-term-examination 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

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

Additional Bibliography

Aston Zhang (Author), Zachary C. Lipton (Author), Mu Li (Author), Alexander J. Smola. Dive into Deep Learning. Cambridge University Press.
Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press.

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