Artificial Intelligence

Presentation Structured probabilistic models also known as probabilistic graphical models (PGMs) are powerful modeling tools for reasoning and decision making with uncertainty. PGMs have many application domains, including artificial vision, natural language processing, efficient coding, and computational biology. PGMs connect graph theory and probability theory and provide a flexible framework for modeling large collections of random variables with complex interactions. This is an introductory course to PGMs focused on two main axes: (1) the role of PGMs as a unifying language in machine learning, which allows for a natural specification of many problematic domains with inherent uncertainty, and (2) the set of computational tools for probabilistic inference (such as making predictions to aid decision making) and learning (estimating the structure of the graph and its parameters from data). Associated skills Basic skills 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 are able to integrate knowledge and face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments. 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. Specific skills E1) Apply the models and algorithms of machine learning, autonomous systems, interaction in natural language, mobile robotics and / or web intelligence to a problem of well-identified interactive intelligent systems. Specifically, models and algorithms for inference and learning in structured graphical models. E3) Identify new uses of models and algorithms in the field of interactive intelligent systems. Specifically, uses for which structured graphical models are appropriate E6) Present the result of a research project in the field of interactive intelligent systems in a scientific forum and in interaction with other researchers. Learning outcomes E1) • Solves problems related to interactive intelligent systems. Identifies the appropriate models and algorithms to solve a specific problem in the field of interactive intelligentsystems. • 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. E3) • Recognizes the intentional domain of application of a model or algorithm in the field of interactive intelligent systems. • Describes limitations of a given model or algorithm for a new problem. • Identifies parallels in problems in the field of interactive intelligent systems. • Transfers the solution of a specific problem in the field of interactive intelligent systems to a similar problem. E6) • Organizes and conducts an oral presentation of a research paper according to the rules of the discipline • Carries out a scientific argument and convincingly defend scientific work in front of an expert and non-expert public.