Checking date: 20/05/2022


Course: 2022/2023

Machine learning for business decision making
(17661)
Study: Bachelor in Management and Technology (351)


Coordinating teacher: ALER MUR, RICARDO

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

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Big data and business analitics
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
Skills and learning outcomes
Description of contents: programme
1. Introduction to Machine Learning 2. Data Extraction and Exploration 3. Basic models for classification and regression 3.1. Nearest neighbor (KNN) 3.2. Trees and rules 4. Methodology: training, hyper-parameter tuning, model evaluation, pre-processing 5. Feature Selection / Generation 6. Advanced models for classification and regression 6.1. Bagging, Random Forest 6.2. Boosting 6.3. Stacking 6.4. Support Vector Machines 7. Unsupervised learning: 7.1. Data clustering 7.2. Associative Learning
Learning activities and methodology
AF1. THEORETICAL-PRACTICAL CLASSES. They will present the knowledge that students should acquire. They will receive the class notes and will have reference texts to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems on the part of the student will be solved and workshops and evaluation tests will be carried out to acquire the necessary skills. AF2. TUTORING. Individual or group. AF3. INDIVIDUAL OR GROUP STUDENT WORK. MD1 THEORY CLASS. Lectures with support of computer and audiovisual media, in which the main concepts of the subject are taught and the materials and bibliography are provided to complement the students' learning. MD2. PRACTICES. Resolution of practical cases individually or in group. MD3. TUTORING. Individual or group. For 6 credit courses, 4 hours with 100% attendance.
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
  • Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal . Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. 2016
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Max Kuhn. Applied Predictive Modeling. Springer. 2013
(*) 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.