Checking date: 10/06/2021

Course: 2021/2022

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

Coordinating teacher: FUENTETAJA PIZAN, RAQUEL

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

Type: Compulsory
ECTS Credits: 6.0 ECTS


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)
* 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
Description of contents: programme
1. Introduction to Machine Learning 2. Classification and regression techniques 2.1. Decision trees and rules 2.2. Regression trees and rules 2.3. Instance based learning 2.4. Classifier ensembles 3. Unsupervised techniques 3.1 Clustering 3.2. Associative learning 4. Reinforcement learning 4.1. Markov Decision Processes 4.2. Q-Learning 5. Relational learning 5.1. Introduction to Inductive Logic Programmimg 6. Methodological issues 6.1. Machine Learning methodology 6.2. Evaluation and hypothesis testing
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): 1,5 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): 2 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
  • D. Borrajo, J. González y P. Isasi. Aprendizaje automático. Sanz y Torres.
  • E. Rich y K. Knight. Artificial Intelligence. McGraw-Hill.
  • S. Russel y P. Norving. Artificial Intelligence: a modern approach. Prentice Hall. 2003
  • T. M. Mitchell. Machine Learning. Mc Graw Hill.
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.

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