Checking date: 25/04/2024

Course: 2024/2025

Machine learning
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: SAEZ ACHAERANDIO, YAGO

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
Basic knowledge of statistics and programming
This course covers the main fundamentals of machine learning, from a very practical approach we are going to work for making a computer to be able to build models that allows it to learn concepts or recognize patterns, and to be able to define them and/or predict new incoming instances, and all this without being programmed explicitly.
Skills and learning outcomes
Description of contents: programme
1. Introduction to machine learning and inductive learning 2. Supervised Learning I: Trees and Decision Rules 3. Evaluation and validation of learning models 4. Machine learning methodology 5. Supervised Learning II: Regression Trees, Instance-Based Learning, and Ensembles of Classifiers 6. Unsupervised and semi-supervised learning techniques 7. Relational machine learning
Learning activities and methodology
Formation activities AF1 - Theoretical class AF2 - Practical classes AF3 - Theoretical and practical classes AF5 - Tutorials AF6 - Group work AF7 - Individual student work AF8 - Partial and final exams -> Presentations and/or partial and final dissertations teaching methodology MD1 - Presentations in the teacher's class with the support of computer and audiovisual media, in which the main concepts of the subject are developed and the bibliography is provided to complement the learning of the students. MD2 Critical reading of texts recommended by the professor of the subject: articles, reports, videos, tutorials, etc., either for later discussion in class, or to broaden and consolidate knowledge of the subject. MD3 Resolution of practical cases, problems, etc. raised by the teacher individually or in groups MD5 Preparation of work and reports individually or in groups
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100

Calendar of Continuous assessment

Basic Bibliography
  • Aurélien Geron. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly. 2017
  • Crish Bishop. Pattern Recognition and Machine Learning. Springer. 2006
  • Murphy, K.P.. . Machine Learning. A Probabilistic Perspective. MIT Press. 2012
Recursos electrónicosElectronic Resources *
(*) 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.