ECE
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New Courses Offered by ECE in 2018

As technology changes and advances, so does the range of courses offered by our faculty. Faculty who are uniquely qualified teach the courses, and bring extensive experience based on their own research in these areas.

EECS 444: Analysis of Societal Networks

4 credits
Instructor: Vijay Subramanian
Prerequisites: EECS 301, MATH 425 or STATS 425, C or better for prerequisites.

This course serves as an introduction to networks such as Twitter, Facebook, BitTorrent, trade networks, supply-chain networks, road networks and more. It covers how these networks are connected, how they form, how processes and transactions take place on them, and how they are being transformed and interconnected in the modern world. Students analyze network processes such as how opinions and fads spread on networks, how sponsored advertisements are developed, how web content is displayed, how recommendation systems work, and more.
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EECS 598: Computational Data Science

3 credits
Instructor: Raj Nadakuditi
Prerequisites: Programming experience in MATLAB, C, C++, Python or R.

This course is an in-depth introduction to computational methods in data science for identifying, fitting, extracting and identifying patterns in large data sets. At the core of the course is understanding the methodology for ‘computational thinking,’ that is, when algorithm is working as it ‘should’ and how and when it might not and, most importantly, how the data might be re-processed for the algorithm to again work.
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EECS 598: Green Photonics

3 credits
Instructor: Zetian Mi
Prerequisites: EECS 429 or equivalent.

This course covers the application of semiconductor optoelectronics including light sources, detectors, and photovoltaic devices to solve society's greatest issues in energy, water, and the environment. This includes solar cells and LED lighting, the fundamentals of semiconductor photonic materials and devices, integrated nanophotonic circuits and solar fuels.
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EECS 598: Hardware for Machine Learning

3 or 4 credits
Instructor: Zhengya Zhang
Prerequisites: EECS 427 or EECS 470.

This course will survey the latest architecture and circuit designs for machine learning applications, including deep convolutional neural nets, spiking neural nets, neuro-inspired designs, RRAM and MRAM, speech and stereo vision applications, graph processing, and point clouds.
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EECS 598: Organic Electronic Devices and Applications

3 credits
Instructor: Stephen Forrest
Prerequisites: Senior level quantum mechanics, junior level electronic devices.

In this course, the basics of the optical and electrical properties of organic semiconductors are reviewed, followed by how organics are deposited and patterned to achieve thin film device structures. The bulk of the class material is concerned with device physics, engineering and applications, including light emission from OLEDs, their various structures and adaptations for high efficiency displays and lighting.
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EECS 598: Control and Modeling of Power Electronics

3 credits
Instructor: Al Avestruz
Prerequisites: Familiarity with classical control concepts.

This course will address the control and modeling of AC-DC, DC-AC, and DC-DC power electronic systems. Topics include small signal models, digital and analog control, switched, sampled data, and averaged models, large signal considerations, distributed power conversion, computer modeling in PLECS, MATLAB/Simulink, and LTSpice, and designing audio switching power amplifiers, peak power point tracking for renewables and energy scavenging, resonant converters for wireless power transfer, and more.
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EECS 598: Beyond CMOS

3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or graduate standing.

In this course, students will discuss the multitude of devices and circuit architectures that may (or may not) replace silicon CMOS in the years to come. Students will survey the devices, circuit architectures, and integration challenges facing the semiconductor industry in the "More than Moore" era.
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EECS 598: Laser Plasma Diagnostics

3 credits
Instructor: Louise Willingale
Prerequisites: EECS 537 or permission of instructor.

In this course, students discuss the techniques used for creating, characterizing and timing high power laser pulses from megajoule-nanosecond pulses to relativistic-intensity femtosecond pulses. This includes exploring the diagnostics used to characterize high-energy density plasmas through optical and other radiation measurements as well as backlighting techniques.
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EECS 598: VLSI for Signal Processing and Communication Systems

3 credits
Instructor: Hun-Seok Kim
Prerequisites: Permission of instructor.

This course will survey methodologies to design energy efficient and high-performance VLSI systems for the state-of-the-art image oraudio processing, machine learning, and wireless communication systems. The primary focus of the course is on designing hardware efficient algorithms and energy-aware VLSI IC architectures to deliver the performance and efficiency requiredby various signal processing applications.
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EECS 598: Random Graphs

3 credits
Instructor: Alfred Hero
Prerequisites: EECS 501 and EECS 551.

This course will cover theory and application of random graphs from the perspective of statistical data science. Random graphs will be presented as empirical models for high dimensional relational and correlational data. Procedures for inferring model parameters of random graphs from observational data will be covered, with applications from machine leanring, clustering and pattern recognition, data mining, biological networks, and social networks.
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May 25, 2018