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  • ESE 617: Non-Linear Control Theory

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Undergraduate Control course. This courses focuses on nonlinear systems, planar dynamical systems, Poincare Bendixson Theory, index theory, bifurcations, Lyapunov stability, small-gain theorems, passivity, the Poincar map, the center manifold theorem, geomentric control theory, and feedback linearization.
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  • ESE 630: Elements of Neural Computation,Complexity,and Learning

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): A semester course in probability or equivalent exposure to probability (e.g. ESE 530). Non-linear elements and networks: linear and polynomial threshold elements, sigmoidal units, radial basis functions. Finite (Boolean) problems: acyclic networks; Fourier analysis and efficient computation; projection pursuit; low frequency functions. Network capacity: Feedforward networks; Vapnik-Chervnenkis dimension. Learning theory: Valiant's learning model; the sample complexity of learning. Learning algorithms: Perception training, gradient descent algorithms, stochastic approximation. Learning complexity: the intractability of learning; model selection.
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  • ESE 632: Random Process Models and Optimum Filtering

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 530 or equivalent. Convergence, continuity, stationarity and second order properties of random processes. Spectral representation. Markov processes, Wiener and Poisson processes. Karhunen-Loeve expansion. Optimum filtering: matched and Wiener filtering, finite observations, spectral factorization. Kalman filtering. Basic concepts of parameter estimation and hypothesis testing.
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  • ESE 635: Distributed Systems

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Basic knowledge of linear systems (ESE 500), linear algebra (MATH 312 or equivalent), and optimization (ESE 504 or equivalent) and some familiarity with basics of nonlinear systems (ESE 617 or equivalent). Students without this background should consult with the instructor before registering. This research seminar deals with tools, methods, and algorithms for analysis and design of distributed dynamical systems. These are large collections of dynamical systems that are spatially interconnected to form a collective task or achieve a global behavior using local interactions. Over the past decade such systems have been studied in disciplines as diverse as statistical physics, computer graphics, robotics, and control theory. The purpose of this course is to build a mathematical foundation for study of such systems by exploring the interplay of control theory, distributed optimization, dynamical systems, graph theory, and algebraic topology. Assignments will consist of reading and resesarching the recent literature in this area. Topics covered in distributed coordination and consensus algorithms over networks, coverage problems, effects of delay in large scale networks. Power law graphs, gossip and consensus algorithms, synchronization phenomena in natural and engineered systems, etc.
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  • ESE 650: Learning in Robotics

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): Students will need permission from the instructor. They will be expected to have a good mathematical background with knowledge of machine learning techniques at the level of CIS 520, signal processing techniques at the level of ESE 531, as weill as have some robotics experience. This course will cover the mathematical fundamentals and applications of machine learning algorithms to mobile robotics. Possible topics that will be discussed include probabalistic generative models for sensory feature learning. Bayesian filtering for localization and mapping, dimensionality reduction techniques formotor control, and reinforcement learning of behaviors. Students are expected to have a solid mathematical background in machine learning and signal processing, and will be expected to implement algorithms on a mobile robot platform for their course projects. Grading will be based upon course project assignments as well as class participation.
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  • ESE 674: Information Theory

    3.00 Credits
    University of Pennsylvania

    Prerequisite(s): ESE 530 or equivalent exposure to probability theory. Deterministic and probabalistic information. The pigeon-hole principle. Entropy, relative entropy, and mutual information. Random processes and entropy rate. The asymptotic equipartition property. Optimal codes and data compression. Channel capacity. Source channel coding. The ubiquitous nature of the theory will be illustrated with a selection of applications drawn from among: universal source coding, vector quantization, network communication, the stock market, hypothesis testing, algorithmic computation and Kolmogorov complexity, and thermodynamics.
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  • ESE 680: Special Topics in Electrical and Systems Engineering

    3.00 Credits
    University of Pennsylvania

    Advanced and specialized topics in both theory and application areas. Students should check Graduate Group office for offerings during each registration period.
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  • ESE 895: Teaching Practicum

    0.50 Credits
    University of Pennsylvania

    Participation of graduate students in the teaching mission of the department will help to develop teaching, presentation, leadership, and interpersonal skills while assisting the department in discharging its teaching responsibilities. All doctoral students are required to participate under faculty guidance in the teaching mission of the department. This requirement will be satisfied by completing two 0.5 course units of teaching practicum (ESE 895). Each 0.5 course unit of teaching practicum will consist of the equivalent of 10 hours of effort per week for one semester. As a part of the preparation for and fulfillment of the teaching practicum requirement, the student will attend seminars emphasizing teaching and communication skills, lead recitations, lead tutorials, supervise laborato experiments, develop instructional laboratories, develop instructional materiaand grade homeworks, laboratory reports, and exams. A teacher training seminar will be conducted the day before the first day of classes of the Fall semester. Attendance is mandatory for all second-year students. As much as possible, the grading aspect of the teaching practicum course will be such as not to exceed 50% of the usual teaching assistant commitment time. Some of the recitations will b supervised and feedback and comments will be provided to the student by the faresponsible for the course. At the completion of every 0.5 course unit of teach, the student will receive a Satisfactory/Unsatisfactory grade and a written evsigned by the faculty member responsible for the course. The evaluation will beon comments of the students taking the course and the impressions of the facult
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  • ESE 899: Independent Study

    3.00 Credits
    University of Pennsylvania

    For students who are studying a specific advanced subject area in electrical engineering. Students must submit a proposal outlining and detailing the study area, along with the faculty supervisor's consent, to the graduate group chair for approval. A maximum of 1 c.u. of ESE 899 may be applied toward the MSE degree requirements. A maximum of 2 c.u.'s of ESE 899 may be applied toward the Ph.D. degree requirements.
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  • ESE 990: Masters Thesis

    10.00 Credits
    University of Pennsylvania

    Register for this only once after all 10 course units are completed. This carries full time status with no credit.
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