Checking date: 26/04/2024

Course: 2024/2025

Machine learning II
Bachelor in Data Science and Engineering (Plan: 392 - Estudio: 350)

Coordinating teacher: PARRADO HERNANDEZ, EMILIO

Department assigned to the subject: Signal and Communications Theory Department

Type: Compulsory
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Programming Machine Learning I
Acquisition of skills in modelling continuous and discrete data Acquisition of skills in the design of non-linear machine learning models based in kernel methods Acquisition of skills in the application and interpretation of latent variable models Acquisition of criteria to decide which advanced machine learning models or families of models should be used in each situation Acquisition of skills to construct prototypes based on probabilistic machine learning or kernel methods oriented to the solution of data processing problems.
Skills and learning outcomes
Description of contents: programme
In the subject we introduce advanced concepts in machine learning. In the first part, we concentrate of nonlinear classification and regression methods. While in the second part, we will focus on advanced topics of non-supervised learning. In the last part of the course, we will cover transfer learning and multitask learning. PART 1: Nonlinear classification and regression Kernel methods Ensemble methods (boosting y random forests) Gaussian Processes for classification and regression PARTE 2: Unsupervised Learning Introduction to graphical models Latent variable models Bayesian nonparametrics PARTE 3: Multi-task learning Transfer learning
Learning activities and methodology
Learning activities: AF1: THEORETICAL-PRACTICAL CLASSES. They will present the knowledge that students should acquire. They will receive the class notes and will have basic texts of reference to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems on the part of the student will be solved and workshops and evaluation test will be held to acquire the necessary skills. AF2: Updated to allegation AF3: INDIVIDUAL OR GROUP WORK OF THE STUDENT. AF9: FINAL EXAM. In which the knowledge, skills and abilities acquired throughout the course will be assessed globally. Learning methodology: MD1: CLASS THEORY. Exhibitions in the teacher's class with support of computer and audiovisual media, in which the main concepts of the subject are developed and the materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical cases, problems, etc. raised by the teacher individually or in groups. MD3: TUTORING. Individualized assistance (individual tutorials) or group (collective tutorials) to students by the teacher.
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment

Extraordinary call: regulations
Basic Bibliography
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer. 2006
Additional Bibliography
  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press. 2012

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