Image credit:
E7 students watching anaglyphs with red/blue glasses


EE290O: Applications of Machine Learning / Reinforcement Learning in Urban Mobility and Mixed Autonomy [3 units]
Description: This course teaches the fundamental techniques of machine learning (ML) / reinforcement learning (RL) required to train multi-agent systems to accomplish autonomous tasks in complex environments.

EECS127/227: Optimization Models in Engineering [3 units]
Description: This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems, such as linear or constrained least-squares optimization.

EE128-ME134: Feedback Control Systems [4 unit]
Description: Analysis and synthesis of linear feedback control systems in transform and time domains. Control system design by root locus, frequency response, and state space methods. Applications to electro-mechanical and mechatronics systems.

CE291F-ME 236-EE 291c: Control and Optimization of Distributed Parameters Systems [3 units]
Description:Distributed systems and PDE models of physical phenomena (propagation of waves, network traffic, water distribution, fluid mechanics, electromagnetism, blood vessels, beams, road pavement, structures, etc.). Fundamental solution methods for PDEs: separation of variables, self-similar solutions, characteristics, numerical methods, spectral methods. Stability analysis. Adjoint-based optimization. Lyapunov stabilization. Differential flatness. Viability control. Hamilton-Jacobi-based control. Also listed as Electrical Engineering 291 and Mechanical Engineering 236. Course Format: Three hours of lecture per week. Prerequisites: Engineering 77, Mathematics 54 (or equivalent), or consent of instructor.

CE 191: Civil and Environmental Engineering Systems Analysis [3 units]
Description:This course is organized around five real-world large-scale CEE systems problems. The problems provide the motivation for the study ofquantitative tools that are used for planning or managing these systems. The problems include design of a public transportation system for an urban area, resource allocation for the maintenance of a water supply system, development of repair and replacement policies for reinforced concrete bridge decks, traffic signal control for an arterial street, scheduling in a large-scale construction project. Course Format: Two hours of lecture and three hours of computer laboratory per week. Prerequisites: 93, Engineering 77. Formerly 152

CE 290Z: Selected Topics in Air Transportation [2 units]
Description: Current developments in air transportation. Topics of current interest, including methods of systems operations analysis, airport andairline planning, and issues of air transportation policy. Course Format: Two hours of lecture per week. Prerequisites: 260 (may be taken concurrently)

E7 (formerly E77): Introduction to computer programming for scientists and engineers [4 units]
Description: Elements of procedural and object-oriented programming. Induction, iteration, and recursion. Real functions and floating-point computations for engineering analysis. Introduction to data structures. Representative examples are drawn from mathematics, science, and engineering. The course uses the MATLAB programming language. Sponsoring departments: Civil and Environmental Engineering and Mechanical Engineering.

CE301: Future Civil and Environmental Engineering Teachers Teaching Workshop [1 unit]
Description: The course includes supervised teaching of laboratory sections of civil engineering courses, group analysis of videotapes, reciprocal classroom visitations, and an individual project.

EE291e: Hybrid and embedded systems [3 units]
Description:The multi-disciplinary research field of hybrid systems has emerged over the last decade and lies at the boundary of computer science, control engineering and applied mathematics. In general, a hybrid system can be defined as a system built from atomic discrete components and continuous components by parallel and/or serial composition, arbitrarily nested. The behaviors and interactions of components are governed by models of computation. The behaviors and interactions of components are governed by models of computation. Hybrid phenomena captured by such mathematical models are manifested in a great diversity of complex engineering applications such as real-time systems, embedded software, robotics, mechatronics, aeronautics, and process control. The high-profile and safety-critical nature of such applications has fostered a large and growing body of work on formal methods for hybrid systems: mathematical logic, computational models and methods and automated reasoning tools supporting the formal specification and verification of performance requirements for hybrid systems, and the design and synthesis of control programs for hybrid systems that are provably correct with respect to formal specifications. This course investigates modeling, analysis and verification of various classes of hybrid systems. Special attention is paid to computational and simulation tools for hybrid systems. Applications ranging from networked sensors, power electronics, avionics, autonomous vehicles will be covered. The course consists of lectures, a handful of homework assignments, and a final project.

Advanced Control and Optimization of Distributed Parameters Systems [3 units]
(taught under course number CE290 in Fall 2010)Ancre