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

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

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.