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Course: 2024/2025

Machine Learning

(15757)

Dual Bachelor in Computer Science and Engineering, and Business Administration (Plan: 437 - Estudio: 233)

Coordinating teacher: ALER MUR, RICARDO

Department assigned to the subject: Computer Science and Engineering Department

Type: Compulsory

ECTS Credits: 6.0 ECTS

Course: 4º

Semester: 2º

Requirements (Subjects that are assumed to be known)

Programming (Course: 1 / Semester: 1) Statistics (Course 2 / Semester: 1) Automata and Formal Language Theory (Course 2 / Semester 1) Artificial Intelligence (Course 2 / Semester 2)

Objectives

* Understand the basic techniques of Machine Learning * Learn to determine when to use Machine Learning in real problems * Learn to determine which technique is appropriate for each problem * Learn to apply the techniques in real problems from a practical point of view

Skills and learning outcomes

Link to document

Description of contents: programme

1. Introduction to Machine Learning 2. Basic classification and regression techniques 3. Advanced classification and regression techniques 3. Methodology (model evaluation, hyper-parameter tuning, preprocessing) 4. Unsupervised techniques (clustering, associative learning) 5. Reinforcement learning

Learning activities and methodology

* Lectures: 1 ECTS. Oriented, among others, towards the competences related to the fundamentals, paradigms and techniques useful to build and evaluate intelligent systems based on Machine Learning. * Practical/Lab sessions: 1 ECTS. Oriented towars the specific instrumental competences and competences about problem solving and application of acquired knowledge. * Continuous assessment tests (individual work): 0,5 ECTS. Oriented towards the competences related to the fundamentals, paradigms and techniques useful to build and evaluate intelligent systems based on Machine Learning. * Practical works (team work): 3 ECTS. Oriented to develop and integrate the specific competences related to the resolution and implementation of practical cases, generating a report including the problem definition, the technique applied, the obtained results and their interpretation. * Tutorials: Individualized or collective tutorials with the teacher. * Final exam: 0,5 ECTS. Its objective is to influence and complement the development of specific cognitive abilities, especially the analysis, design, representation and formalization of knowledge and the application of techniques for solving problems.

Assessment System

% end-of-term-examination 30
% of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment

Final grade will be composed of 40% of individual work and 60% of team work. The individual work will consider both the individual activities performed during the course and a final exam. A minimum calification in the individual work will be required.

Specifically, the activities to develop are:

- Partial exams (10%):  exams with theoretical content, to evaluate the knowledge acquired by the students trough the use of basic and advanced bibliography.

- Final exam (30%): theoretical-practical exam that requires a global knowledge about the main machine learning concepts.

- Tutorials and assignments (60%):  tutorials will be about the use of machine learning tools and techniques; practices wiil be about practical applications that require the representation of knowledge for the analysis, design and implementation of a computing solution in intelligent systems based on machine learning.

The final grade is the summation of the partial exam plus assignments plus final exam. In order to pass this course, it is necessary both:
- To obtain at least 50% from the summation of all grades (partial exam, assignments, final exam)
- To obtain at least 3 points out of 10 in the Final Exam.

Extraordinary call: regulations

Basic Bibliography

Ian H Witten, Eibe Frank, Mark A Hall, Christopher J Pal. Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc.. 2016
D. Borrajo, J. González y P. Isasi. Aprendizaje automático. Sanz y Torres.
S. Russel y P. Norving. Artificial Intelligence: a modern approach. Prentice Hall. 2003
Sebastian Raschka. Machine Learning with PyTorch and Scikit-Learn. Packt Publishing. 2022

Electronic Resources *

Scikit-learn team · Scikit-learn: Machine Learning in Python : https://scikit-learn.org/stable/

Additional Bibliography

Basilio Sierra Araujo (Ed.). Aprendizaje automático: conceptos básicos y avanzados. Aspectos prácticos utilizando el software WEKA. Pearson Education.
J. W. Shavlik y T. G. Dietterich (eds.). Readings in Machine Learning. Morgan Kaufmann.
P. W. Langley. Elements of Machine Learning. Morgan Kaufmann.
R. Sutton and A Barto. Reinforcement Learning: an Introduction. Kluwer Academic Publishers.
Saso Dzeroski y Nada Lavrac. Relational Data Mining. Springer Verlag.

(*) 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.