Checking date: 10/06/2021


Course: 2021/2022

Machine learning in data mining
(13728)
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

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Programming II
Objectives
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.
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The course syllabus may change due academic events or other reasons.