Checking date: 20/05/2022

Course: 2022/2023

Machine learning in data mining
Study: Bachelor in Statistics and Business (203)

Coordinating teacher: ALER MUR, RICARDO

Department assigned to the subject: Department of Computer Science and Engineering

Type: Electives
ECTS Credits: 6.0 ECTS


Requirements (Subjects that are assumed to be known)
Programming II
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
Skills and learning outcomes
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
Learning activities and methodology
Theory: Lectures will be focused on teaching all concepts related to machine learning. They will be carried out in synchronous on-line mode. 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 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Calendar of Continuous assessment
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. 2019
  • Brett Lantz. Machine Learning with R. Packt Publishing. 2019
  • Max Kuhn. Applied Predictive Modeling. Springer. 2013
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Hadley Wickham, Garrett Grolemund, . R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media. 2016
Recursos electrónicosElectronic Resources *
Detailed subject contents or complementary information about assessment system of B.T.
(*) 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.