Syllabus query

Academic Year/course: 2017/18

439 - Bachelor's Degree in Informatics Engineering

30231 - Machine Learning

Syllabus Information

Academic Year:
30231 - Machine Learning
Faculty / School:
110 - Escuela de Ingeniería y Arquitectura
439 - Bachelor's Degree in Informatics Engineering
Second semester
Subject Type:

5.1. Methodological overview


The learning process is based on the teachers' lectures, and the students' work during the practical sessions. In both cases, previous personal work is essential. Before each lecture, students should study and understand previous lectures. Before each practical session, students should analyse the assignement, perform some preliminary work, and identify the parts that require further clarifications from the teacher. At the end of each practical sessions, students should present the results obtained.


5.2. Learning tasks

The average students' work required for this course is 150 hours: 

  • Lectures (type T1)  (30 hours).
  • Practical sessions (type T3) (30 hours).
  • Personal work (type T7) (80 hours). 
  • Examinations (type T8) (10 hours).


5.3. Syllabus

  1. Supervised Learning. Regression
  2. Regularization and model selection
  3. Logistic regression
  4. Generative models. Naive Bayes
  5. Anomaly Detection
  6. Non supervised learning. PCA
  7. Clustering
  8. Recommender systems
  9. Non parametric methods. Gaussian processes
  10. Big Data

5.5. Bibliography and recommended resources

[BB: Bibliografía básica / BC: Bibliografía complementaria]

  • [BB] 2. Duda, Richard O.. Pattern classification / Richard O. Duda, Peter E. Hart, David G. Stork . - 2nd ed. New York [etc.] : John Wiley and Sons, cop. 2001
  • [BB] Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
  • [BB] Murphy, Kevin P.. Machine learning : a probabilistic perspective / Kevin P. Murphy . Cambridge [etc.] : The MIT Press, cop. 2012
  • [BC] Alpaydin, Ethem. Introduction to machine learning / Ethem Alpaydin . 3rd ed. Cambridge [etc.] : MIT Press, cop. 2014
  • [BC] Bishop, Christopher M.. Pattern recognition and machine learning / Christopher M. Bishop . [1st ed., 13th print.] New York : Springer, 2009

Listado de URL

  • Transparencias y apuntes de la asignatura, enunciados de problemas, casos de estudio y Guiones de prácticas[]