Courses taught by Professor Ahmed at CU Boulder:

Graduate Courses

ASEN 6519 (special topics): Probabilistic Algorithms for Aerospace Autonomy

(Spring 2019, Spring 2017)

Advanced graduaaagraphicate course covering modern probabilistic learning and AI techniques that allow autonomous systems to reason under uncertainty. Topics include: probabilistic graphical models (Markov models, Bayesian networks); batch/offline learning for pattern recognition and `static’ decision making; approximate inference methods; sequential optimal decision making (MDPs, POMDPs); online learning (multi-armed bandits, reinforcement learning); and advanced topics as time permits (e.g. human-autonomy interaction; advanced probabilistic inference/planning methods).

ASEN 5044: Statistical Estimation for Dynamical Systems

(Fall 2020, Fall 2018, Fall 2017, Fall 2016)

Introduces theory and methods of statistical estimation for  dynamical systems, with emphasis on aerospace applications. Major kflooptrackingradartopics 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, unscented Kalman filters, and general Bayesian filters for non-linear systems.

ASEN 5014: Linear Control Systems

(Fall 2021, Fall 2015, Fall 2014, Spring 2014) umichstatespacetutorial_observerdesign_blockdiagram

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): Advanced State Estimation

(Spring 2021, Spring 2020) Covers principles and techniques of advstateest_graphicdiscrete-time Bayesian state space estimation “go beyond” traditional least-squares and Kalman filtering approaches for dynamical systems characterized by partial observability and non-Gaussian uncertainties, which arise in many applications defined by complex non-linear stochastic dynamics and measurement processes. Topics include in depth treatments of: nonlinear least squares and maximum likelihood point estimation theory; principles of Bayesian estimation theory and recursive filtering; statistical linearization and unscented/sigma point filtering; sequential Monte Carlo and particle filtering techniques; Gaussian mixture filtering; data association methods for target tracking; and decentralized Bayesian state estimation.

Undergraduate Courses

ASEN 3128: Aircraft Dynamics

(Spring 2021, Fall 2019, Spring 2019, Spring 2018, Spring 2017, Spring 2016)

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 alsof16chino2006 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.