Some fundamental numerical method for engineering are taught
ANALYSIS OF ALGORITHMS
A fundamental computer science course on algorithm correctness, complexity and intractability
Automata Theory and Formal Languages
A fundamental computer science course on the theory of abstract machines and formal languages. Topics of decidability and undecidability are covered.
INTRODUCTION to Robot PROGRAMMING
A graduate course in introductory robotics and robot programming. Kinematics, dynamics and basic control concepts are covered with a focus on programming and building a basic robotic simulation.
Deep REINFORCEMENT LEARNING
A Reinforcement Learning course with a focus on function approximation with (deep) neural networks. In addition to value based RL methods, policy gradient methods that can be applied to continuous state and continuous action problems are also covered.
INTRODUCTION to Neural Networks and MACHINE LEARNING
A classical neural network course covering fundamental neural network architectures and machine learning methods.
Theory of COMPUTATION
A course on theory of computation spanning CS333 and CS410 with more depth
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
The aim of this course is to introduce students main concepts and techniques of Artificial Intelligence (AI). The course targets equipping the students with the ability of building intelligent computational systems. Major topics of the course include: intelligent agents, heuristic search, game playing, constraint satisfaction, uncertain knowledge and reasoning, decision making and machine learning.
COLLECTIVE DECISION MAKING IN MULTI-AGENT SYSTEMS
This course provides an overview of collective decision making within multi-agent systems and its main concepts, theories, and algorithms. It covers utility theory, preference aggregation, voting methods, principles of automated negotiation, and group recommender systems.