Checking date: 16/12/2023


Course: 2024/2025

Introduction to Machine Learning
(19603)
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)
EPI


Coordinating teacher: GOMEZ VERDEJO, VANESSA

Department assigned to the subject: Signal and Communications Theory Department

Type: Additional training
ECTS Credits: 2.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
None
Objectives
The goal of this course is that the student acquires the basics of machine learning, knows some of the basic methods and learns the procedures for parameter selection and model evaluation.
Skills and learning outcomes
Description of contents: programme
Python as a programming language for machine learning. Machine learning pipeline: preprocessing, model training, parameter validation and evaluation metrics. Linear and polynomial regression models. Basic classification models: Logistic regression, decision trees. Unsupervised learning: PCA and K-means.
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams METHODOLOGY MD1: Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the course are developed and complemented with bibliography. MD2: Critical reading of texts recommended by the professor of the course. MD3: Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the course, as well as case studies. MD5: Elaboration of works and reports individually or in groups.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60

Calendar of Continuous assessment


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