Checking date: 25/05/2022


Course: 2022/2023

Machine Learning
(15757)
Study: Bachelor in Computer Science and Engineering (218)


Coordinating teacher: ALER MUR, RICARDO

Department assigned to the subject: Department of Computer Science and Engineering

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming (Course: 1 / Semester: 1) Statistics (Course 2 / Semester: 1) Automata and Formal Language Theory (Course 2 / Semester 1) Artificial Intelligence (Course 2 / Semester 2)
Objectives
* Understand the basic techniques of Machine Learning * Learn to determine when to use Machine Learning in real problems * Learn to determine which technique is appropriate for each problem * Learn to apply the techniques in real problems from a practical point of view
Skills and learning outcomes
Link to document

Description of contents: programme
1. Introduction to Machine Learning 2. Classification and regression techniques 2.2. Nearest Neighbor methods 2.2. Decision trees and rules 2.4. Ensembles 3. Methodology 4. Unsupervised techniques 4.1. Clustering 4.2. Associative learning 5. Reinforcement learning: 5.1. Markov decision processes 5.2. Q-learning 6. Relational Learning
Learning activities and methodology
* Lectures: 1 ECTS. Oriented, among others, towards the competences related to the fundamentals, paradigms and techniques useful to build and evaluate intelligent systems based on Machine Learning. * Practical/Lab sessions: 1 ECTS. Oriented towars the specific instrumental competences and competences about problem solving and application of acquired knowledge. * Continuous assessment tests (individual work): 0,5 ECTS. Oriented towards the competences related to the fundamentals, paradigms and techniques useful to build and evaluate intelligent systems based on Machine Learning. * Practical works (team work): 3 ECTS. Oriented to develop and integrate the specific competences related to the resolution and implementation of practical cases, generating a report including the problem definition, the technique applied, the obtained results and their interpretation. * Tutorials: Individualized or collective tutorials with the teacher. * Final exam: 0,5 ECTS. Its objective is to influence and complement the development of specific cognitive abilities, especially the analysis, design, representation and formalization of knowledge and the application of techniques for solving problems.
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Calendar of Continuous assessment
Basic Bibliography
  • Ian H Witten, Eibe Frank, Mark A Hall, Christopher J Pal. Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc.. 2016
  • D. Borrajo, J. González y P. Isasi. Aprendizaje automático. Sanz y Torres.
  • S. Russel y P. Norving. Artificial Intelligence: a modern approach. Prentice Hall. 2003
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Basilio Sierra Araujo (Ed.). Aprendizaje automático: conceptos básicos y avanzados. Aspectos prácticos utilizando el software WEKA. Pearson Education.
  • J. W. Shavlik y T. G. Dietterich (eds.). Readings in Machine Learning. Morgan Kaufmann.
  • P. W. Langley. Elements of Machine Learning. Morgan Kaufmann.
  • R. Sutton and A Barto. Reinforcement Learning: an Introduction. Kluwer Academic Publishers.
  • Saso Dzeroski y Nada Lavrac. Relational Data Mining. Springer Verlag.
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN


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