Artificial Intelligence

Basic Competences: • That students have and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context. • That students are able to integrate knowledge and face the complexity of making judgments from information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of its knowledge and judgments. • That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous. Transversal Competences • Applying with flexibility and creativity the acquired knowledge and adapting it to new contexts and situations. Specific Competences • Solve mathematical problems related to machine learning and apply the knowledge to different forms of learning (supervised, unsupervised, Bayesian machine learning). • Ability to communicate effectively using the technical vocabulary of the field in English. • Use techniques of calculus and linear algebra applied to machine learning by means of existing software packages. • Apply machine learning to realistic problems in order to learn appropriate models. • Identify machine learning problems and select the appropriate algorithm for solving them. • Understand the knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context. • That students know how to apply the knowledge acquired and their ability to solve problems in novel or poorly known environments within broader (or multidisciplinary) contexts related to their area of study. • That students have the learning skills that allow them to continue studying in a way that will need to be largely self directed or autonomous. Learning outcomes • Understand the mathematical principles that form the basis of machine learning. • Solve basic mathematical exercises related to machine learning theory. • Recognize the type of learning problem and select appropriate algorithms. • Implement machine learning algorithms in a common programming language and test them on actual learning problems. • Evaluate and interpret the outcome of learning on a given problem and compare the outcome for different algorithms. • Select appropriate values of hyper-parameters through validation. Apply models and algorithms for sequential decision making in reactive environments. Solves problems related to RL. Identifies the appropriate models and algorithms to solve a specific problem in the field of RL Evaluates the result of applying a model or algorithm to a specific problem. Presents the result of the application of a model or algorithm to a specific problem according to scientific standards. Identify new uses of models and algorithms in the field of RL. Specifically, uses that lend themselves to a formulation as sequential decision making. Recognizes the intentional domain of application of a model or algorithm in the field of reinforcement learning. Describes limitations of a given model or algorithm for a new problem. Identifies parallels in problems in the field of RL. Transfers the solution of a specific problem in the field of interactive intelligent systems to a similar problem. Present the result of a research project in the field of RL in a scientific forum and in interaction with other researchers. Organizes and conducts an oral presentation of a research paper according to the rules of the discipline. Carries out a scientific argument and convincingly defends scientific work in front of an expert and non-expert public