Electrical Engineering and Computer Science

CSE Course Announcements

Fall 2016: Introduction to Autonomous Robotics

Course No.: EECS 398-004
Credit Hours: 3 credits
Instructor: Chad Jenkins
Prerequisites: Permission of instructor

Course Description:
This course covers the essentials of robot modeling and autonomy. [More Info]

Fall 2016: Fundamentals of the Internet

Course No.: EECS 398-001
Credit Hours: 2 credits
Instructor: Mohammed Islam
Prerequisites: None

Course Description:
This course introduces students to the fundamentals of the internet. You use the internet every day, and in this house we permit you to "look under the hood" of the internet. By taking this course you will have a better appreciation of how computer networks work and how your computer communicates over the internet. [More Info]

Fall 2016: Electric Distribution Systems

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463

Course Description:
This course covers the fundamentals of electric power distribution systems and electric loads. Topics to be covered include introduction to distribution grids, power flow in distribution grids, distribution transformers, fundamentals of electric loads, and electric load modeling. [More Info]

Fall 2016: Hybrid Systems: Specification, Verification and Control

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Necmiye Ozay
Prerequisites: EECS 562 or (EECS 560 + permission of instructor)

Course Description:
This course will provide a working knowledge of several analysis and design techniques to guarantee safety, reliability and performance of hybrid systems. [More Info]

Fall 2016: Mining Large-Scale Graph Data

Course No.: EECS 598-004
Credit Hours: 4 credits
Instructor: Danai Koutra
Prerequisites: Basic knowledge of linear algebra, programming and machine learning

Course Description:
This course will cover recent methods and algorithms for analyzing large-scale graphs, as well as applications in various domains (e.g., neuroscience, web science, social science, computer networks). The focus will be on scalable and practical methods, and students will have the chance to analyze large-scale datasets. [More Info]

Fall 2016: Power Semiconductor Devices

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or equivalent or graduate standing

Course Description:
Power devices are at the heart of all modern electronics, from the grid and renewable energy sources to fuel-efficient vehicles and mobile devices. This course will cover semiconductor switches and rectifiers for discrete and integrated power electronics. [More Info]

Fall 2016: Advanced Topics in Electric Drives

Course No.: EECS 598-007
Credit Hours: 4 credits
Instructor: Heath Hofmann
Prerequisites: EECS 560 (co-requisite)

Course Description:
This course will cover advanced topics in electric drives, such as:

* Nonlinear modeling of electric machines, and subsequent controller design

*Discrete-time control implementations of field-oriented control techniques

*Real-time parameter estimation for online condition monitoring of electric machines

Students will gain hands-on experience with these techniques in the Power and Energy Instructional Laboratory. The course will have a final project where students will design and implement their own control algorithm. [More Info]

Fall 2016: Quantum Nanotechnology

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Duncan Steel
Prerequisites: MATH 215 and 216, PHYSICS 240, co-req EECS 230 or permission

Course Description:
The development and application of nano-technology is impacting nearly all the fields of engineering, from those who are developing it to those who use it. Future engineers working to design new devices will need a skill set that is considerably broadened to include the behavior of materials and devices when they become sufficiently small. Devices like transistors and quantum well lasers have already forced engineers to understand the impact of Fermi-Dirac statistics and energy quantization on devices. However, the emergent field of nano-technology is revealing that the concepts we have from our current scale devices is no longer adequate to predict correct device experience. Moreover, in this new regime, new physical properties are emerging that may revolutionize how we think of information and its storage, transmission and processing. This course aims to introduce students to basic concepts in quantum physics that our relevant to novel device concepts. The course will explore the new properties of nano-vibrators, quantum LC circuits, the role of loss, the impact of the quantum vacuum on nano-switches, coherent superposition, quantum entanglement and light, one photon at a time. [More Info]

Fall 2016: An Introduction to Networks

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Vijay Subramanian
Prerequisites: EECS 203 and EECS 301 (or equivalent) recommended

Course Description:
This course serves as an introduction to the broad class of networks: 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. [More Info]

Fall 2016: Learn to be a Software Consultant

Course No.: EECS 498-005
Credit Hours: 3 credits
Instructor: Elliot Soloway
Prerequisites: Senior status in CSE

Course Description:
As part of UMichs Innovate Blue, the School of Information offers a Design Clinic (see description below) where budding entrepreneurs come with software projects and receive UI/UX consulting from SI students. However, the entrepreneurs oftentimes have questions about software design and development (questions about prototyping tools, underlying architecture, etc.).

In this 498, then, CSE students would serve two 2.5 hour/week shifts in the Design Clinic providing software design/development consulting to entrepreneurs and they would create template apps as demos; in addition, CSE students will participate in learning sessions with the other consultants. CSE students will develop consulting skills as they provide real consulting to users of the Design Clinic.

Requirements: Senior Status in the CSE major. For permission to register, please contact Elliot Soloway: soloway@umich.edu [More Info]

Winter 2016: Multidisciplinary Capstone Design Project - Supplemental Information

Course No.: EECS 498-006 and EECS 498-007
Credit Hours: 3 credits
Instructor: Tony Grbic or Greg Wakefield
Prerequisites: See PDF

Course Description:
See attached PDF [More Info]

Winter 2016: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites: EECS student

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects. This pilot douse is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for ECE masters students interested in meaningful project experiences.

For WN16, we will have several projects with a biomedical focus as well as energy, sports, spaceflight, and other areas needing EECS students (e.g. sensor/electronics, embedded systems, controls, and wireless). Please contact Prof. Gilchrist with questions. [More Info]

Winter 2016: Information Retrieval and Web Search

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Rada Mihalcea
Prerequisites: EECS 281

Course Description:
This course will cover traditional material, as well as recent advances in Information Retrieval (IR), the study of indexing, processing, querying, and classifying data. Basic retrieval models, algorithms, and IR system implementations will be covered. [More Info]

Winter 2016: Introduction to Autonomous Robotics

Course No.: EECS 398-002
Credit Hours: 3 credits
Instructor: Chad Jenkins
Prerequisites: Linear algebra (MATH 214, 217, 417, 419) and data structures (EECS 281 or equivalent)

Course Description:
This course will cover the essentials of robot modeling and autonomy. See flyer website for more details. [More Info]

Winter 2016: Social Computing Systems

Course No.: EECS 498-008
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: EECS 493 or permission of instructor

Course Description:
Computation rarely exists in isolation. From social media, to collaboration and coordination tools, to crowdsourcing and collective intelligence, technology has risen from use as an individual tool for focused domains to play a role in or even mediate a majority of social interactions today. Social Computing is the study of this interplay between social processes and the computation that supports and augments them. This course will cover topics including social media, systems for supporting collective action, data mining and analysis, crowdsourcing, human computation, and peer production. [More Info]

Winter 2016: Computing for Computer Scientists

Course No.: EECS 398-003
Credit Hours: 1 credit
Instructor: Pat Pannuto
Prerequisites: None

Course Description:
Learn the tools that every computer scientist should know: Shells, Scripting, Makefiles, Version Control, Compilers, Text Editors, Debugging. This class is a 1 credit seminar meeting weekly on Fridays from 1:30-2:30, designed for early-career EECS students. [More Info]

Winter 2016: Power System Markets & Optimization

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463

Course Description:
This course covers the fundamentals of electric power system markets, and the optimization methods required to solve planning and operational problems including economic dispatch, optimal power flow, and unit commitment. The course will highlight recent advances including convex relaxations of the optimal power flow problem, and formulations/solutions to stochastic dispatch problems. Problems will be placed in the context of actual electricity markets, and new issues, such as incorporation of renewable resources and demand response into markets, will be covered. All students will conduct an individual research project. [More Info]

Winter 2016: Intelligent Interactive Systems (IIS)

Course No.: EECS 498-002
Credit Hours: 4 credits
Instructor: Emily Mower Provost
Prerequisites: EECS 280 or permission of instructor

Course Description:
Today's world is becoming increasingly automated. This includes not only explicit interactions with automated systems, but also implicit sensing that accompanies many popular technologies. Explicit interactions include speech-based question answering with Siri and Google Voice. But what can we learn implicitly? How can we take advantage of the wealth of pervasive and ubiquitous computing platforms? How can we leverage distributed sensor environments? These are the questions that increasingly underlie Intelligent Interactive Systems (IIS). The focus of this class will be on providing methods that can be used to answer these questions and a semester-long project that ties these questions together through a new interactive technology. [More Info]

Winter 2016: Learn To Be A Software Consultant By Doing Consulting!

Course No.: EECS 498-009
Credit Hours: 3 credits
Instructor: Elliot Soloway
Prerequisites: Senior status in CSE

Course Description:
As part of UMichs Innovate Blue, the School of Information offers a Design Clinic (see description below) where budding entrepreneurs come with software projects and receive UI/UX consulting from SI students. However, the entrepreneurs oftentimes have questions about software design and development (questions about prototyping tools, underlying architecture, etc.).

In this 498, then, CSE students would serve two 2.5 hour/week shifts in the Design Clinic providing software design/development consulting to entrepreneurs and they would create template apps as demos; in addition, CSE students will participate in learning sessions with the other consultants. CSE students will develop consulting skills as they provide real consulting to users of the Design Clinic. [More Info]

Winter 2016: Advanced Topics and Design of Power Electronics

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Al-Thaddeus Avestruz
Prerequisites: EECS 418 and EECS 460 or equivalents

Course Description:
This class will address some advanced topics and techniques in power electronics and the craft of design through case studies. Topics may include switched capacitor circuits, resonant power conversion, magnetics, wireless power transfer, and instrumentation, among other. Advanced methods in the analysis, manufacturing, and control of power electronics will also be discussed. [More Info]

Winter 2016: Topics in Optoexcitonic Engineering

Course No.: EECS 598-009
Credit Hours: 3 credits
Instructor: Parag Deotare
Prerequisites: Introductory electromagnetics and solid state physics or permission of instructor

Course Description:
This seminar course will review recent research and developments on topics in Nanophotonics and Excitonic Engineering with potential applications in data communication and processing. Topics covered will be related to engineering interaction of light with nanoscale systems, optical interactions between nanosystems and resonance phenomenon. Students read research papers followed by a brief lecture introducing the important related concepts before the papers are open for discussion in the class. Students/teams will also spend last 4 weeks investigating a current research problem posed by a faculty member. This will entail reading and spending time in the faculty lab and will be followed by a presentation to the full class. [More Info]

Winter 2016: Hands On Robotics

Course No.: EECS 498-003
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: Engineering and science seniors and grad students

Course Description:
Hands On Robotics is a robotics course based on building robots using the CKBot modular robot system. The course will cover basic concepts in robotics: kinematics, control, programming and design.

Open to EECS seniors and up; all other engineering and science seniors and graduate students with permission of instructor. [More Info]

Winter 2016: Carbon Nanoelectronics and Nanophotonics

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Zhaohui Zhong
Prerequisites: EECS 520 or permission of instructor

Course Description:
Carbon based nanomaterials, in particular carbon nanotube and graphene, have generated great excitements over the past decade due to their unique electrical, optical and mechanical properties. This special topic course introduces theories and experimental works on carbon nanotube and graphene based electronic and photonics devices. The course will also have two student labs of testing graphene and nano electronics. [More Info]

Winter 2016: Organic Electronics: From Fundamentals to Applications

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Steve Forrest
Prerequisites: Senior level quantum physics, solid state physics, electricity and magnetism

Course Description:
In this course, we will trace the history, science and modern applications of organic electronic technology. The first half of the course is dedicated to understanding the fundamentals of organic semiconductor materials. This includes consideration of crystal structure, bonding forces, and structure-property relationships of both small molecule and polymer semiconductors. We then provide a comprehensive description of the physics leading to their unique optical and electrical properties. What are the characteristics that make organic semiconductors (sometimes known as excitonic materials) different from conventional semiconductors such as Si and GaAs? The second half of the course concentrates on applications that exploit the unique characteristics of organics. We focus particularly on light emission in OLEDs, and how electron spin plays a significant role in organics, particularly in contrast to inorganic semiconductors. Then we address light detection in photodetectors and solar cells. Will the potentially low cost of these devices ultimately lead to their widespread use? Finally, we will examine advances in thin film transistors, lasers, and even molecular electronic devices, and their prospect for use in new, and even traditional optoelectronic applications. [More Info]

Winter 2016: Random Matrix Theory

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 551 or linear algebra, basic probability

Course Description:
This course covers the theory and algorithms emerging from the study of random matrices as it is currently applied in signal processing, machine learning, statistics and science. Topics include random sample covariance matrices, random graphs, spectral limit theorems such as Wigner's semi-circle and Marcenko-Pastur laws, free probability, randomized numerical linear algebra, matrix statistics, passage to the continuum limit, moment methods, matrix completion and compressed sensing. [More Info]

Winter 2016: Information Science

Course No.: EECS 398-001
Credit Hours: 4 credits
Instructor: Clayton Scott
Prerequisites: MATH 116 and (ENGR 101 or equivalent)

Course Description:
This course will examine the basic mathematical theory of information, and apply that theory to understand several modern technologies for information processing and analysis.

Projected Syllabus: 4-5 weeks: Essentials of Shannons information theory, including entropy, data compression, transmission over noisy channels, and error correcting codes 2-3 weeks: Encryption, from historical ciphers to modern crypto systems 3-4 weeks: Extracting information from data: information retrieval and machine learning 3-4 weeks: Frequency concepts: Fourier analysis, AM and FM radio, sampling and reconstruction, spectrum spreading, and digital signal processing [More Info]

Fall 2015: Randomness and Computation

Course No.: EECS 598-04
Credit Hours: 4
Instructor: Grant Schoenebeck
Prerequisites: EECS 376

Course Description:
Randomness and the tools of probability theory have proven central in many areas of modern science, including, perhaps surprisingly, the design and analysis of algorithms. This course will be organized around the main tools and techniques (linearity of expectation, the second moment method, Chernoff bounds, martingales, Lovasz-Local Lemma, Monte Carlo Markov Chain, etc) used in probabilistic analysis of algorithms. Along the way, students will be exposed to a large variety of classic theoretical computer science works resulting from the applications of these same tools to both randomized algorithms and the analysis of random combinatorial objects (e.g. graphs, Boolean formulae) and deterministic algorithms applied to random inputs drawn from some distribution.

Advanced applications covered may include the Talagrands inequality; social networks; streaming algorithms; distributed algorithms; quantum computation; approximation algorithms; semidefinite programs; cryptographic protocols, and more. Specific advanced topics included will depend on the interests of the students.

If you are interested in theoretical computer science (TCS) or tools of probabilistic analysis, it should be a fun course. It will assume basic theory understanding (at the level of 376) and basic probability theory, and the methodology will be that of formal mathematical proofs. The course will be targeted as an introductory course for CSE graduate students studying theory (very broadly speaking)though others should benefit as well, including advanced undergraduates and graduate students from other areas. This course will count for a theory breadth requirement CSE masters and PhD students and for a depth requirement for PhD students. See course website for more information. [Full Story]

Fall 2015: Graph Mining and Exploration at Scale: Methods and Applications

Course No.: EECS 598-012
Credit Hours: 4 Credits
Instructor: Danai Koutra
Prerequisites: Prerequisites for Lec 012: Basic knowledge of Linear Algebra, Probability Theory/Statistics, and Programming (e.g., Python, JAVA, Matlab, R) or Permission of Instructor

Course Description:
Graphs naturally represent information ranging from links between webpages to friendships in social networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we extract useful knowledge, understand the underlying processes, and make interesting discoveries?

This course will cover recent models and algorithms for exploring and making sense of large graphs, as well as applications in various domains (e.g., web, social science, computer networks, neuroscience). The focus will be on scalable, practical, but also principled methods, and students will have the chance to analyze large-scale datasets. The topics that we will cover include ranking, label propagation, clustering and community detection, summarization, similarity, and anomaly detection.

Fall 2015: Human Computation and Crowdsourcing Systems

Course No.: 598-011
Credit Hours: 4 credits
Instructor: Walter Lasecki
Prerequisites: Programming fluency; senior undergraduate or graduate standing in either EECS, or Permission of Instructor

Course Description:
Using human intelligence to solve computational tasks -- also called human computation -- has enabled the creation of software systems that go well beyond the current boundaries of artificial intelligence (AI). Making open recruitment calls to large, often heterogeneous, groups of people (crowdsourcing) has allowed human computation to be scaled to provide on-demand services and even real-time responses. This course will cover the core work in human computation and crowdsourcing, with a focus on techniques for creating interactive intelligent systems that are powered by a combination of human and machine intelligence. We will also touch on the theory underlying many of the current approaches (e.g., game theory, voting theory, and machine learning), and potential ethical concerns raised by these systems (e.g., ensuring fair wages, and end-user privacy)."

Fall 2015: Data Science for Medicine

Course No.: EECS 498-005
Credit Hours: 3 credits
Instructor: Zeeshan Syed
Prerequisites: EECS281 or equivalent

Course Description:
With increasing amounts of medical data becoming available there is an opportunity to significantly reduce the burden imposed by major diseases in a data-driven manner. This course provides students with a hands-on introduction to computational advances offering significant improvements in our ability to understand, diagnose, and treat major healthcare conditions. During the semester we will explore several foundational topics in data science for medicine, including data representation, data manipulation, data analysis, and data visualization with a review of organ system physiology and common medical data elements. Students will be introduced to these topics during lectures, with the class focusing on breadth instead of a focus on any single topic in depth to provide an opportunity to sample and apply data science techniques. The course also focuses on providing students with a significant opportunity to investigate the application of these ideas to real-world clinical challenges. Students will be expected to supplement theory in data science for medicine with a semester long project on actual medical data. Students will be encouraged to think creatively about traditionally hard problems and required to perform group research exposing them to designing practical data science systems for medical care. Students will also be exposed to research and potential entrepreneurship opportunities beyond the class.

Fall 2015: Plasmonics

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Somine Eunice Lee
Prerequisites: None

Course Description:
Plasmonics is the study of optical phenomena related to the electromagnetic response of conductors. The furled of plasmonics has recently been accelerated by the rapid advancements in nano fabrication. The interaction of light with nanoscale objects renders unique optical, electronic, magnetic and thermal properties useful to a wide range of areas, including electrical engineering, biomedical engineering, chemical engineering, mechanical engineering, material science, chemistry and physics. [More Info]

Fall 2015: Power System Dynamics and Control

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or permission of instructor

Course Description:
This course will introduce angle and voltage stability concepts and consider control strategies for improving dynamic performance. It will provide and overview of nonlinear dynamical systems, including geometrical properties of solutions, Lyapunov methods for approximating the region of attraction, and bifurcation analysis. [More Info]

Fall 2015: Foundations of Computer Vision

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Jason Corso
Prerequisites: Graduate standing or permission of instructor

Course Description:
Computer Vision seeks to extract useful information from images, video and other visual content. This course will introduce the breadth of modern computer vision through a few foundational problems that span various topic areas. Examples of possible foundational problems include image formation and projective geometry, robust model fitting, perceptual priors, matching and similarity, invariance, motion and multi view geometry. The foundational problems will be tied to specific applications such as feature extraction, segmentation, structure from motion, and action recognition. [More Info]

Fall 2015: Hybrid Systems: Specification, Verification and Control

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Necmiye Ozay
Prerequisites: EECS 562 or EECS 560 + permission of instructor

Course Description:
Hybrid systems, dynamical systems where continuous dynamics and discrete events interact, are ubiquitous and can be found in many different contexts. Examples are as diverse as manufacturing processes, biological systems, energy systems, medical devices, robotics systems, automobiles and aircrafts. Advances in computing and communications technologies have enabled engineering such systems with a high degree of complexity. Most of these systems are safety-critical, hence their correctness must be verified before they can be deployed. This course will provide a working knowledge of several analysis and design techniques to guarantee safety, reliability and performance of such systems. [More Info]

Fall 2015: An Introduction to Social, Economic and Technological Networks

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Vijay Subramanian
Prerequisites: EE203 and/or EECS 301 are recommended

Course Description:
Networks are everywhere. We encounter a variety of networks of different sizes and forms on a daily basis: societal networks such as the network of retweets of a certain has tag on Twitter or the friends network on Facebook; technological networks such as the Internet with the telecommunication network of computers, the links between webpages, the groupings of users generated by recommendation systems for predictions or the network of users on BitTorrent downloading a specific file; and economic networks such as trade networks or supply-chain networks. Some of these networks emerge naturally such as many societal networks, while others are planned such as the public transportation or road network. We depend on the efficient functioning of these networks to transact many of our activities.

This course serves as an introduction to the broad class of networks described above: how these networks are connected, how they form, how processes and transactions take place on them, and how they are being transferred and interconnected in the modern world. Students will learn how to develop and apply mathematical models and tools from graph theory, linear algebra, probability and game theory in order to analyze network processes such as how opinions and fads are spread on networks, how sponsored advertisements are developed, how web content is displayed, how recommendation systems work, etc. [More Info]

Fall 2015: EECS 498: Control of Manufacturing Systems

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Semyon Meerkov
Prerequisites: Elementary probability theory

Course Description:
Manufacturing is a major source of national wealth. Losing manufacturing, a country is losing its wealth. Until recently, methods of design and control of manufacturing systems has been based on "weak" engineering - experience, common sense, and, in some cases, simulations. Efficient manufacturing requires more: rigorous analytical methods. Such methods have emerged during the last 25 years. The results obtained, with emphasis on control and management, will be discussed in the course.

The course is directed towards undergraduate students from all CoE departments interested in careers involving design/manufacturing of products, e.g. automobiles, aircraft, semiconductors, computer/communication devices, etc. The skill acquired should make the students knowledgable in various facets of manufacturing and marketable as engineering managers of manufacturing operations. [More Info]

Winter 2015: Waves & Imaging in Random Media

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: John Schotland
Prerequisites: Basic partial differential equations; some knowledge of probability theory

Course Description:
This is a special topics course. The focus is on the theory of wave propagation in in homogenous media in various asymptotic regimes including: (i) geometrical optics of high-frequency waves (ii) homogenization of low-frequency waves in periodic and random media (iii) radiative transport and diffusion theory for high-frequency waves in low-frequency random media. Applications to inverse problems in imaging will be considered. [More Info]

Winter 2015: Collabrified Mobile Apps for K-12

Course No.: EECS 498-008
Credit Hours: 3 credits
Instructor: Elliot Soloway
Prerequisites: Senior status in CSE

Course Description:
In this course, students will create apps to support learners in K-12. The apps will employ the Collabrify SDK that students at UMich developed -- a software development kit that enables a developer to take an app that is meant as a solo-user app and turn that app into one that supports two or more simultaneous users!! [More Info]

Winter 2015: Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498-005
Credit Hours: 3 or 4 credits
Instructor: Brian Gilchrist
Prerequisites: Permission of instructor

Course Description:
This pilot course is about providing students real-world, multidisciplinary design project opportunities to satisfy their MDE requirement and for EE masters students interested in meaningful project experiences. [More Info]

Winter 2015: Advanced Topics in Analog ICs

Course No.: EECS 598-005
Credit Hours: 4 credits
Instructor: David Wentzloff and Michael Flynn
Prerequisites: EECS 413 and co-requisite EECS 522

Course Description:
This course will cover design and analysis of advanced analog and mixed-signal integrated circuits, beyond what is covered in EECS 511 and EECS 522. [More Info]

Winter 2015: Network Information Theory

Course No.: EECS 598-006
Credit Hours: 3 credits
Instructor: Sandeep Pradhan
Prerequisites: EECS 501 or equivalent

Course Description:
This course aims to develop a set of mathematical tools to study communication problems that arise in networks. A strong emphasis will be put on obtaining an intuitive framework to think about these problems. This course is aimed at graduate students working in the areas of electrical engineering, computer science, statistics, and mathematics. [More Info]

Winter 2015: Performance Systems: Mobile Phones as Musical Instruments

Course No.: EECS 498-003 and EECS 598-003
Credit Hours: 3 or 4 credits
Instructor: Georg Essl
Prerequisites: EECS 493 or graduate standing or permission of instructor

Course Description:
In this course, you will design your own mobile phone musical instruments, write your own pieces for this new genre, and develop mobile music performance practice in a unique blend of music performance and engineering. [More Info]

Winter 2015: Embedded Systems: An Application-Centered Approach

Course No.: EECS 598-002
Credit Hours: 4 credits
Instructor: Robert Dick
Prerequisites: EECS 311 or 312 or 373 or 482 or equivalent or permission of instructor

Course Description:
Embedded systems are computers within other devices such as wearable devices, automobiles, sensor networks, and medical devices. The focus of this course is to give students an understanding of the process of going from an idea to a product or research finding in the field of embedded systems. [More Info]

Winter 2015: Probabilistic Graphical Models for Vision and Beyond

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Jason Corso
Prerequisites: EECS 501 or graduate-level proficiency with probability and statistics

Course Description:
This course will cover probabilistic graphical models in detail starting from the basics and pushing through contemporary results. There will be an emphasis on driving problem formulations from computer vision but our coverage will be broad; connections to other application areas will be discussed when plausible. [More Info]

Winter 2015: Grid Integration of Alternative Energy Sources

Course No.: EECS 498-002
Credit Hours: 4 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 215 or EECS 314 or permission of instructor

Course Description:
This course will present a variety of alternative energy sources, along with energy processing technologies that are required for power system connection. Topics will be covered at a level suited to establish a broad understanding of the various technologies, and of the associate system implications. [More Info]

Winter 2015: Control of Discrete Event Systems

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Stephane Lafortune
Prerequisites: EECS 566 or EECS 598-005 in Fall 2013 or permission of instructor

Course Description:
This course will cover advanced topics on control of discrete event systems, with focus on the following topics: distributed and decentralized control architectures; synthesis methodologies for controllers under safety and liveness properties; comparison of synthesis techniques for specifications described by automata and by temporal logics; joint control and diagnosis problems for fault-tolerant control; discussion of relevant case studies. [More Info]

Winter 2015: Information Retrieval and Web Search

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Rada Mihalcea
Prerequisites: EECS 281

Course Description:
This course will cover traditional material, as well as recent advances in Information Retrieval (IR), the study of indexing, processing, querying, and classifying data. [More Info]

Fall 2014: Foundations of Computer Vision

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Jason Corso
Prerequisites: Permission of instructor

Course Description:
Computer Vision seeks to extract useful information from images. This course begins the fundamentals of image formation and then organizes the remaining material according to the class of information to be extracted. The course has been designed to present an introduction to computer vision targeted to graduate students. The course will balance theory and application both in lectures and assignments. [More Info]

Fall 2014: Practical Machine Learning

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Jenna Wiens
Prerequisites: EECS 445 or EECS 545 or permission of instructor

Course Description:
In this seminar class we will cover the basics of practical machine learning and data mining while focusing on real-world applications. We will read and critique recent applied ML work in the fields of sports analytics, data-driven medicine, finance, and personalized education. At the same time, we will review a complementary set of papers to help guide our discussion in terms of the pragmatic aspects of ML e.g., feature engineering, cross-validation, and performance measures. The overall goal of the class is for students to gain a deeper understanding of the practical challenges and pitfalls associated with applying machine learning tools and techniques in a real-world setting.

Fall 2014: Probabilistic Analysis of Large Scale Systems

Course No.: EECS 598-006
Credit Hours: 3 credits
Instructor: Vijay Subramanian
Prerequisites: EECS 501 or permission of instructor

Course Description:
This course will focus on emerging topics in epidemics and diffusions, queueing systems, analysis of randomized algorithms, Bayesian information cascades, network analysis and random graphs. [More Info]

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