Courses taught by Professor Ahmed at CU Boulder:
ASEN 6519 (special topics): Algorithms for Aerospace Autonomy
(new: Spring 2017)
Advanced graduate course covering modern statistical learning and AI techniques that allow autonomous systems to reason under uncertainty. Topics include: probabilistic models (Markov models, Bayesian networks, Markov random fields, factor graphs, decision graphs, Bayesian nonparametrics); batch/offline learning for pattern recognition and `static’ decision making; approximate inference methods (extended/unscented Kalman filters, variational Bayes, Monte Carlo methods); sequential optimal decision making (MDPs, POMDPs); online learning (multi-armed bandits, reinforcement learning); and advanced topics as time/interest permits (distributed multi-agent reasoning; human-autonomy interaction; explainable and introspective AI).
ASEN 5044: Statistical Estimation for Dynamical Systems
(offered each fall, starting Fall 2016)
Introduces theory and methods of statistical estimation for dynamical systems, with emphasis on aerospace applications.
Major topics include: in depth review of applied probability and statistics; discrete time linear dynamical systems and stochastic processes; optimal state estimators for dynamical systems; theory and design of Kalman filters for linear systems; linearized Kalman filters, extended Kalman filters, and general Bayes filters for non-linear systems.
ASEN 5014: Linear Control Systems
(Spring 2014, Fall 2014, Fall 2015)
Modeling, analysis, and design of continuous-time control systems using the state space approach. Vector spaces, linear operators, and linear equation solution theory are used to describe system solutions and their stability, controllability, and observability properties. State observers and state feedback control are developed, along with an introduction to linear-quadratic optimal control.
ASEN 6519 (special topics): Model-based Parameters and State Estimation
(Spring 2015) Covers core techniques for extracting and combining information from noisy sensor data and known system models: batch and recursive estimation methods (including linear and non-linear least-squares); maximum likelihood and maximum a posteriori point estimation methods; and Bayesian techniques, with emphasis on Kalman filtering and related filters for discrete-time dynamical systems. In addition to theoretical considerations, estimator design and performance evaluation are discussed in the context of real applications for tracking and vehicle systems.
ASEN 3128: Aircraft Dynamics
(Spring 2016, Spring 2017)
Covers fundamentals of atmospheric flight dynamics and control for rigid body aircraft. Develops mathematical models, tools, and techniques for analyzing aircraft stability and control. The course covers an understanding of linearized theory and numerical techniques for nonlinear problems. The course also covers equations of motion, static and dynamic stability of an aircraft, linearization and analysis of linear systems. Specific topics include: nomenclature; aircraft static stability; Euler angles; aircraft equations of motion and linearization; stability derivatives; linear dynamical system analysis; analysis of longitudinal and lateral dynamics; modal approximations; open-loop aircraft response to actuation; and stability augmentation and feedback control design.