* 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
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.