Checking date: 10/06/2021

Course: 2021/2022

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.) KNOWLEDGE: - To know the basics of extracting knowledge from data - To know the different tasks that can be solved with machine learning - To know the different techniques of machine learning and their characteristics - To know the methodology of knowledge extraction and the phases involved - To know tools available for extracting knowledge 2.) UNDERSTANDING: - To understand the basic concepts of knowledge extraction - To understand the basics and motivations of data mining - To understand the methodology and the different phases of knowledge extraction - To understand the usefulness of different techniques for extracting knowledge - To understand the differences of different representations: propositional and relational - To understand the relationship between model complexity, amount of data, characteristics of the problem and overfitting 3.) APPLICATION: - Analyze the domain and design knowledge extraction processes adapted to the problem. - Evaluate the performance and efficiency of different methods of extracting knowledge - Work on specific domains and compare different techniques to verify their performance in extracting knowledge 4.) EVALUATION - Selection of algorithms, model selection and parameter adjustment. - Consider the relationship between computational cost and marginal improvement of different solutions - Assessment of whether the results are adequate, compared to random 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. Machine learning with R and with the MLR (mlr3) library. 5. Advanced methods for classification and regression 5.1. Ensembles: bagging, random forests, boosting 5.2. Support Vector Machines 6. Brief introduction to machine learning in Python (scikit-learn library)
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 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
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
  • Hefin I. Rhy. Machine Learning with R, the tidyverse, and mlr. Manning Publications. 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.