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Course: 2025/2026

Introduction to Robot Learning

(17843)

Master in Robotics and Automatization (Plan: 296 - Estudio: 77)

EPI

Coordinating teacher: CASTILLO MONTOYA, JOSE CARLOS

Department assigned to the subject: Systems Engineering and Automation Department

Type: Electives

ECTS Credits: 3.0 ECTS

Course: 1º

Semester:

Requirements (Subjects that are assumed to be known)

Programming (C, C++, Python, Matlab, etc...).

Objectives

The main goal of this course is to introduce the main techniques and applications of machine learning in robotics. We will study the main areas in which machine learning is employed nowadays, paying also attention to recent approaches such as Deep Learning.

Learning Outcomes

Link to document

Description of contents: programme

1. Introduction - Why is Machine Learning useful in robotics? - Supervised learning - Unsupervised learning 2. Techniques for data classification - Support Vector Machines - K-nearest neighbors - Naïve Bayes 3. Regression techniques for data prediction - Decision trees - Linear and non-linear models - Neural networks 4. Clustering techniques for grouping data and detecting patterns - K-means - Markov hidden models - Gaussian mixture models 5. New trends: Deep Learning 6. Machine learning applications in robotics - Human-Robot Interaction - Autonomous vehicles - Medicine

Learning activities and methodology

Magistral classes, laboratory practical sessions, individual tutorials, and personal work from the students

Assessment System

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

The assessment system in this course will follow the continuous evaluation model, where participation in the classroom, laboratory performance and a final test for assessing the knowledge obtained will be combined in the final grade. It will be necessary to obtain a minimum grade of 3 in the final exam to take the average with the continuous assessment.

Basic Bibliography

Alpaydin, Ethem . Introduction to machine learning. MIT Press. 2010
John Paul Mueller and Luca Massaron. Machine Learning For Dummies. John Wiley & Sons. 2016
Sonia Chernova, Andrea L. Thomaz. Robot Learning from Human Teachers. Morgan & Claypool Publishers. 2014

Additional Bibliography

Vishnu Nath, Stephen E. Levinson. Autonomous Robotics and Deep Learning. Springer Science & Business Media. 2014
Yasser Mohammad and Toyoaki Nishida. Data Mining for Social Robotics: Toward Autonomously Social Robots. Springer. 2016

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