ESE 1110 Introduction to Atoms, Bits, Circuits, and Systems

[Course Catalog]
ESE 1110 is an introduction to the principles underlying electrical and systems engineering. Concepts used in designing circuits, processing signals, analyzing networks, and understanding complex systems will be discussed in lectures and illustrated in the laboratory. This course will provide some of the necessary foundations for students interested in more advanced courses in ESE. The class time also includes an amazing lineup of speakers (faculty, alums, entrepreneurs) to introduce you to careers in research and industry with majors in electrical engineering, computer engineering and systems engineering. The amazing student community includes Penn Electric Racing, Architechs, and PennApps. Checkout the following video that highlights the amazing ESE Lab program

ESE 3010 Introduction to Probability

[Course Catalog]
This course introduces students to the foundations of probability and its rich applications. The topics covered include: discrete and continuous probability spaces , distributions, mass functions, densities; conditional probability; independence; the Bernoulli schema: the binomial, Poisson, and waiting time distributions; uniform, exponential, normal, and related densities; expectation, variance, moments; conditional expectation; generating functions, characteristic functions; inequalities, tail bounds, and limit laws. But a bald listing of topics does not do justice to the subject: the material is presented in its lush and glorious historical context, the mathematical theory buttressed and made vivid by rich and beautiful applications drawn from the world around us. The student will see surprises in election-day counting of ballots, a historical wager the sun will rise tomorrow, the folly of gambling, the sad news about lethal genes, the curiously persistent illusion of the hot hand in sports, the unreasonable efficacy of polls and its implications to medical testing, and a host of other beguiling settings.

ESE 5000 Linear Systems Theory

[Course Catalog]
This graduate-level course focuses on continuous and discrete n-dimensional linear control systems in a time domain based on linear operators. The course is structured around six modules which include a review of linear algebra, complete system response of linear systems with inputs and outputs (both time invariant and time varying), internal, Lyapunov and input-output stability notions and criteria, controllability and observability, control via state and output feedback, observer and estimator design, and the theory of linear quadratic regulator. TIme permitting we also discuss optimal state estimation and Kalman filtering.

ESE 6170 Nonlinear Control Theory

[Course Catalog]
The course provides a basic understanding of nonlinear systems phenomena and studies analysis and control design problems of nonlinear systems. The main analysis tools that will be presented are Lyapunov theory for stability, including the well known LaSalle's invariance principle, and barrier function theory for safety of both autonomous and non-autonomous systems. Further topics include input-output stability, passivity, and the center manifold theorem. The main control tools that will be presented are feedback linearization, backstepping, as well as recent results on learning control Lyapunov and control barrier functions from data. Examples will be taken from mechanical and robotic systems.

ESE 6180 Learning for Control

[Course Catalog]
This course will provide students an introduction to the emerging area at the intersection of machine learning, dynamics, and control. We will investigate machine learning and data-driven algorithms that interact with the physical world, with an emphasis on a holistic understanding of the interplay between concepts from control theory (e.g., feedback, stability, robustness) and machine learning (e.g., generalization, sample-complexity). Topics of study will include learning models of dynamical systems, using these models to robustly meet performance objectives, optimally refining models to improve performance, and verifying the safety of machine learning enabled control systems. The course will also expose students to the ethical considerations that need to be considered when designing learning algorithms that interact with and are placed in feedback with the world. The course will consist of lectures, and students will be evaluated based on traditional and programming assignments, as well as a final project.