Checking date: 24/01/2022

Course: 2022/2023

Machine Learning
Study: Dual Bachelor in Computer Science and Engineering, and Business Administration (233)


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 Statistics Automata and Formal Language Theory Artificial Intelligence
¿ Ability to solve problems, both individually and in a team (PO a,b,c,d,e,k) ¿ Work in teams to analyze and design computer solutions (PO a,b,c,d) ¿ Ability to analyze and synthesize (PO a,b,c) ¿ Ability of organization and planning (PO b,c,d) ¿ Ability of information management (information acquisition and analysis) (PO a,b,k) ¿ Ability to make decisions (PO a,b,c,d,e) ¿ Motivation for quality and continuous improvement (PO b) ¿ Oral and written communication (PO g) ¿ Critical reasoning (PO a,b,d) ¿ Basic knowledge on machine learning (PO a) ¿ Ability to interpret functional specifications towards the development of machine learning based applications (PO a,b,c,e) ¿ Perform detailed analysis and design of computer applications based on machine learning techniques (PO a,b,c,e,k) Learning results: 1. Problem solving, both individually and in group 2. Analysis and design of machine learning systems 3. Oral exposition of lectures works 4. Work to collect and analysis information
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. Machine learning in problem solving 5.1. Macro-operators 5.2. Case Based Reasoning 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 knowledge of concepts, relations among them, techniques to be used, or ways to analyze and synthesize knowledge (PO a) - Practice (3 ECTS) Oriented, among others, towards the competences related to work in teams, problem solving, work organization, or oral (presentation in public of projects or homeworks) and written communication (written reports on their homeworks and projects) (PO b,c,d,e,g,k) - Individual work (2 ECTS) Oriented, among others, towards the competences related to planning, analysis, synthesis, critic reasoning, or concept acquisition (PO a,c,e,g)
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.