Checking date: 28/04/2025 12:13:07


Course: 2025/2026

Machine learning in data mining
(13728)
Bachelor in Statistics and Business (Study Plan 2018) (Plan: 400 - Estudio: 203)


Coordinating teacher: ALER MUR, RICARDO

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming II
Objectives
1.) OF KNOWLEDGE: - Know the different tasks that can be solved with machine learning - Know machine learning techniques and their typology - Know the methodology of machine learning and the phases it entails - Know tools available for machine learning 2.) UNDERSTANDING: - Understand the fundamentals and motivations of machine learning - Understand the work methodology and the different phases of machine learning - Understand the usefulness of different machine learning techniques - Understand the relationship between model complexity, amount of data, problem characteristics and overlearning 3.) APPLICATION: - Analyze the domains and design knowledge extraction processes according to the problem. - Evaluate the performance and efficiency of the different machine learning methods - Work on specific domains and contrast different techniques to check their performance in machine learning 4.) CRITICISM OR ASSESSMENT - Selection of algorithms, selection of models and adjustment of parameters. - Consider the relationship between computational cost and marginal improvement of different solutions - Assessment of whether the results obtained are adequate, compared with chance or basic algorithms
Description of contents: programme
1. Introduction to Machine Learning 2. Basic methods for classification and regression: 2.1. Nearest neighbour (KNN) 2.2. Trees and Rules 3. Machine Learning pipeline 3.1. Training 3.2. Hyper-parameter tuning 3.3. Evaluation 3.4. Preprocessing and feature selection 4. Advanced methods for classification and regression 4.1. Ensembles: bagging, random forests, boosting 4.2. Support Vector Machines Note: Python + scikit-learn will be used for practices
Learning activities and methodology
Theory: Lectures will be focused on teaching all concepts related to machine learning. Practical computer Sessions: The practical classes will be developed so that, in a supervised way, students learn to solve real problems with machine learning. The practices will be carried out in groups of 2 students. There are several assignments related to topics in the course. There will be tutorials to help the understanding both of theory and practice.
Assessment System
  • % end-of-term-examination/test 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70

Calendar of Continuous assessment


Extraordinary call: regulations
Basic Bibliography
  • Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media. 2022
  • Max Kuhn. Applied Predictive Modeling. Springer. 2013
  • Sebastian Raschka. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing. 2022
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Ian H. Witten. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. 2025
Recursos electrónicosElectronic Resources *
Detailed subject contents or complementary information about assessment system of B.T.
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The course syllabus may change due academic events or other reasons.