Bachelor’s Degree: Computer Science and Engineering - Machine Learning - Semester 2

Undergraduate course, Universidad Carlos III de Madrid, Departamento de Informatica, 2016

(3/4 academic year)

Machine Learning


  • 2015/2016: Colmenarejo (Lab teacher)
  • 2015/2016: Leganes (Additional lab teacher)
  • 2014/2015: Colmenarejo (Lab teacher)
  • 2014/2015: Leganes (Lab teacher)


  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

Competences and skill

  • 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)
  • 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