Checking date: 09/05/2018

Course: 2019/2020

Introduction to Robot Planning
Study: Master in Robotics and Automation (77)


Department assigned to the subject: Department of Systems Engineering and Automation

Type: Electives
ECTS Credits: 3.0 ECTS


Students are expected to have completed
Programming (C, C++, Python, Matlab, etc...).
Competences and skills that will be acquired and learning results.
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.
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 neighbours - 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 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
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 and the academic weekly planning may change due academic events or other reasons.