This graduate course explores the mathematical foundations of reinforcement learning (RL), covering classical tabular methods and modern deep RL. Topics include value functions, Bellman equations, reward engineering, environment design, convergence guarantees, and scalability challenges.
Introduces design and verification of safety-critical cyber-physical systems using formal modeling, specification languages, and verification techniques for discrete, continuous, and hybrid dynamical systems.
A core graduate-level course introducing foundational and advanced algorithms, along with rigorous analysis techniques.
Graduate-level introduction to Automata Theory, Computability, and Complexity. The course assumes mathematical maturity. In Fall 2021, the curriculum was expanded to include topics from computational learning theory.
Introductory theory course covering formal models of computation (finite automata to Turing machines), computability, and complexity. Designed for undergraduate students with minimal theoretical background.
Core course introducing theoretical foundations and practical implementation of basic and advanced data structures.
Comparable to CSCI 3434 at CU Boulder. Covered automata theory and foundational logic for computation.
Undergraduate course covering combinational and sequential circuit design, including synchronous and asynchronous logic.
Graduate seminar focused on modeling and verification of cyber-physical systems, covering hybrid systems and formal verification tools.