Programming Probability and Data Analysis

* Understand basic Machine Learning techniques * Learn to determine when to use Machine Learning on real problems * Learn to determine which technique is appropriate for each problem * Learn to apply the techniques in a practical way to real problems Competences: CB1: The students must demonstrate to understand knowledge in an area of study which origin is the secondary education, and will be in a level that, supported with books and other bibliographic references, includes aspects in the frontiers of knowledge. CB2: The students know to apply their knowledge to their work in a professional way and own the competences usually required to solve problems in its area of study CB3: The students own the capacity to interpret relevant data to elaborate claims that include an analysis in social, scientific and ethics topics CB4: The student can transmit information, ideas, problems and solutions to both specialized and non-specialized audience. CB5: Students have developed the learning capabilities to begin new studies with a high degree of autonomy CE13: Capacity to apply and design machine learning methods in classification, regression and clustering for tasks in supervised, unsupervised and reinforcement learning CE2: Capacity to correctly identify predictive problems for a given data and goals, and use the basic results of the regression analysis as a fundamental predictive method. CE3: Capacity to correctly identify classification problems associated to specific goals and data, and to use the results of multivariate analysis as a basics of the classification, clustering and dimensionality reduction methods CG1: Knowledge and abilities to analyze and synthesize basic problems related with engineering and data science, and to solve them and report the results CG2: Knowledge of basic scientific and technical topics that enable for learning new methods and technologies CG3: Capability to solve problems with initiative, decision making, creativity, and communication skills, understanding the ethical, social and professional responsabilities of the data management. Leading, innovation and entrepreneurship capabilities. CG4: Capability to solve technological, computing, mathmatical and statistic problems which can arise in engineering and data science. CG5: Capability to solve problems formalized mathematically and applied to different topics, using numeric algorithms and computational methods. CG6: Capability to synthesize conclusions obtained from performed analysis, and to report in a clear and convincing way, both orally and written. RA1: Advanced knowledge and comprehensiono of the theoreticat and practical aspects of the working methodology in the area of data science with a depth that close to the frontier of knowledge RA2: Capability to apply knowledge in complex working environments and specialized areas which require the use of creative and innovative ideas. RA3: To have the capability to collect and understand data and information over which to create conclusions including, when needed, a reflection about social, ethic or scientific issues. RA4: To be able to manage complex situations which require the development of new solutions both in academic and professional environments in its area of study RA5: To know how communicate to different audiences knowledge, methodologies, ideas, problems and solutions in a clear and precise way RA6: Be able to identify his/her own formative requirements in is area of study or professional environment, and to organize its own learning process with a high degree of autonomy in different contexts.

K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. K4: Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations, in the field of data storage, management and processing. K5: Ability to understand and relate fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S3: Ability to solve technological, computer, mathematical and statistical problems that may arise in data engineering and science, applying knowledge of mathematics, probability and statistics, programming, databases, and languages, grammars and automata. S4: Ability to solve mathematically formulated problems applied to various subjects, using numerical algorithms and computational techniques, and applying knowledge of: algebra; geometry; differential and integral calculus; numerical methods; numerical algorithms; statistics and optimization S5: Ability to correctly identify predictive problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of regression analysis as the basis for prediction methods S6: Ability to correctly identify classification problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of multivariate analysis as the basis for classification, clustering and dimension reduction methods S9: Apply, design, develop, critically analyze and evaluate machine learning methods in classification, regression and clustering problems and for supervised, unsupervised and reinforcement learning tasks. S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences C2: To develop those learning skills necessary to undertake further studies with a high degree of autonomy. C3: Ability to solve problems with initiative, decision making, creativity, and to communicate and transmit knowledge, skills and abilities, understanding the ethical, social and professional responsibility of the data processing activity. Leadership capacity, innovation and entrepreneurial spirit C5: Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.

· Introduction to machine learning · Learning decision trees and rules · Methodological aspects · Learning regression trees and rules · Ensembles of learning methods · Frequent itemsets and association rules · Reinforcement learning · Relational learning

AF1: Presential classes, with theoretical and practical contents AF3: Student work AF8: Practical labs. AF9: Final exam. Evaluation of the abilities that have been acquired along the course. MD1: Classes with theoretical contents MD2: Practices, with cases and problems MD3: Individual and group tutories MD6: Lab practices with support of assistant