These are the courses I have been teaching at Instituto Superior Técnico over the past few years.
Artificial Intelligence and Decision Systems
Objectives: Provide the students with knowledge on basic methods in Artificial Intelligence. Introduce the notion of intelligent agent. Study methodologies for problem-solving, knowledge representation, reasoning, planning, and learning. Understand the studied methodologies in the framework of decision-making systems, covering both symbolic and probabilistic frameworks.
Objectives: The fundamental concepts involved in systems composed by several physical agents with diverse degrees of autonomy (sensors, processors, actuators, robots) spatially distributed are covered. Methods for mapping and representing maps are described. Fundamental concepts and methods for self-localization under uncertainty of the observations and motion model are introduced. Methods for integrating information coming from several sensors, for positioning and map representation of the environment where the sensors are located are presented, as well as methods for problem solving in cooperative systems, including cooperative perception and assignment, planning, and coordination of tasks. The course closes with a integrative perspective of the various taught modules.
Objectives: To introduce the fundamental concepts of robot manipulators and mobile robots: kinematics, dynamics, differential kinematics, path and trajectory planning, control. Presentation of the diverse sorts of sensors useful in robotics, their functioning principle and observation model. Notions of formation control. Programming languages and robot operation interfaces.
Objectives: To acquire basic knowledge about machine learning in general, and about several machine learning techniques. To acquire the capacity to use those techniques in applications and to choose the techniques that are more adequate for each situation.
Modeling and Simulation
Objectives: To give the students a broad perspective on system modeling and simulation (in continuous or discrete time, with continuous or discrete space states, driven by time or events) that they will need throughout their studies and professional life. To address the theory and the practical tools for modeling, characterizing and simulating a large number of real systems (continuous, discrete-event or hybrid) such as physical processes, energy generation systems, electromechanical systems, computer and communication networks, biological/natural systems and manufacturing systems.
Signals and Systems
Objectives: To introduce the basis of Signals and Time-invariant Linear Systems theory. To acquire the ability of analyzing and manipulating signals and linear time-invariant systems in both time and frequency domains, and using system representations based on Laplace and Z transforms.