Electrical Engineering and Computer Science

CSE Course Announcements

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]

Fall 2014: Laser Plasma Diagnostics

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Louise Willingale
Prerequisites: EECS 537 or permission of instructor

Course Description:
This course will cover the techniques used for creating, characterizing and timing high power laser pulses from megajoule-nanosecond pulses to relativistic-intensity femtosecond pulses. [More Info]

Fall 2014: Hands-On Robotics

Course No.: EECS 498-001
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: MATH 216 or permission of instructor

Course Description:
This course will cover basic concepts in robotics, such as kinematics, control, programming and design. [More Info]

Fall 2014: EECS 598-001 Analysis of Electric Power Distribution Systems and Loads

Course No.: EECS 598-001
Credit Hours: 3 credits
Instructor: Johanna Mathieu
Prerequisites: EECS 463 or equivalent

Course Description:
This course covers the fundamentals of electric power distribution systems and electric loads. We will start with an introduction to distribution grids, including their components, typical topologies, and operational strategies. Other topics include power flow in distribution grids and transformers as well as electric loads, including electric load modeling, analysis, and control methodologies. [More Info]

Fall 2014: EECS 598-002 Power Semiconductor Devices

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Becky Peterson
Prerequisites: EECS 320 or permission of instructor

Course Description:
In this course, you will explore semiconductor devices for both discrete and integrated power electronics. Power switches and rectifiers including the power MOSFET, IGBT, HEMT, thyristors, Schottky and pin diodes, as well as emerging power devices will be covered. [More Info]

Fall 2014: Applied matrix algorithms for signal processing, data analysis and machine learning

Course No.: 453
Credit Hours: 4
Instructor: Raj Nadakuditi
Prerequisites: EECS 301 or MATH 425 or STATS 215 or STATS 412 or STATS 426 or IOE 265 or equivalent

Course Description:
Theory and application of matrix algorithms to signal processing, data analysis and machine learning. Theoretical topics include subspaces, eigenvalue and singular value decomposition, projection theorem, constrained, regularized and unconstrained least squares techniques and iterative algorithms. Applications such as image deblurring, ranking of webpages, image segmentation and compression, social networks, circuit analysis, recommender systems and handwritten digit recognition. Greater emphasis on applications than in EECS 551. [More Info]

Winter 2014: EECS 498-003 Multidisciplinary Capstone (MDE) Design Pilot

Course No.: EECS 498-003
Credit Hours: 3-4 credits
Instructor: Brian Gilchrist
Prerequisites: Permission of instructor, senior or grad standing recommended

Course Description:
EECS students, together with ME and MSE students, work on common, interesting, significant major design experience (MDE) projects.

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.

For WN14, many of the projects (though not all) will have a biomedical theme that will require EECS students especially with interests in sensors, embedded systems, and wireless.

Please contact Professor Gilchrist with any questions. [More Info]

Winter 2014: EECS 598-007 Infrastructure for Vehicle Electrification

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or 314

Course Description:
This course covers the fundamentals of the physical and cyber infrastructures that will underpin large-scale integration of plug-in electric vehicles. [More Info]

Winter 2014: EECS 498-002 Intelligent Interactive Systems

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Emily Mower Provost
Prerequisites: EECS 280 or graduate standing

Course Description:
The focus of the course is developing effective speech-based user modeling for interactive systems. We will focus on a series of assistive domains that demonstrate the societal benefit of work in this field, including applications in: depression, autism, and aphasia. Topics will include basic speech modeling, feature handling techniques, data classification, visualization, and interactive system design. [More Info]

Winter 2014: EECS 598-006 Plasmonics

Course No.: EECS 598-006
Credit Hours: 3 credits
Instructor: Somin Lee
Prerequisites: Permission of instructor

Course Description:
This course will review Maxwells equations for the electric and magnetic fields in conductors at low frequencies. Students will be introduced to nanofabrication, including top-down and bottom-up fabrication techniques. Students will also be introduced to characterization techniques of nanoscale objects, including electron microscopy, atomic force microscopy and near field microscopy. Finally, optical, electronic, magnetic, thermal and biomedical applications of plasmonics will be discussed. [More Info]

Winter 2014: EECS 598-005 Statistical Learning Theory

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Clayton Scott
Prerequisites: EECS 501 or equivalent

Course Description:
In this course we will prove performance guarantees that quantify the ability of a machine learning algorithm to generalize from training data to unseen test data. Potential topics to be covered include concentration of measure, uniform deviation bounds, empirical and structural risk minimization, Rademacher complexity, Vapnik-Chervonenkis theory, consistency and rates of convergence, margin-based bounds, stability bounds, and application of these theories to learning algorithms such as decision trees, boosting, support vector machines, and kernel density estimators. [More Info]

Winter 2014: Random matrix theory, algorithms and signal processing applications

Course No.: 598
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 551 or Linear Algebra equivalent, 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.

There will be a special focus on presenting the theory in a manner that facilitates the development of new applications and allowing students that already have a topic in mind to apply these ideas to their topic.

Emerging applications in signal processing, network analysis, wireless communications and statistical physics will be discussed.

The course requirement will be a term project. Students will form teams of two or work individually. Each team will select a project topic, will study a set of papers related to the topic, will write a critique of the papers, and will give an oral presentation at the end of the semester.

No textbook is required for this course. Throughout the lectures papers will be distributed to the class, and references to the relevant literature will be given.

For more information about this course please contact the instructor. [More Info]

Winter 2014: EECS 498-001 Retrieval and Web Search

Course No.: EECS 498-001
Credit Hours: 3 credits
Instructor: Rada Mihalcea
Prerequisites: EECS 216, EECS 401 and EECS 451

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. While the course will primarily focus on IR techniques for textual data, it will also address IR for other media, including images/videos, music/audit files, and geospatial information.

The course will also address topics in Web search, including Web crawling, link analysis, search engine development, social media, and crowd sourcing. Throughout the course, there will be two or three invited lectures from people working at major companies in the field (e.g., Google, Yahoo, Microsoft, Facebook, Twitter). [More Info]

Fall 2013: Prediction and Learning: Its Only a Game

Course No.: EECS 598-006
Credit Hours: 3
Instructor: Jacob Abernethy
Prerequisites: N/A

Course Description:
This course will focus on the problem of prediction, learning, and decision making, yet the underlying theme will involve game playing, betting and minimax analysis. We will explore several classic algorithms -- e.g. Boosting, Multiplicative Weights, the Perceptron -- through this game-theoretic lens. We will begin by introducing the classical Weighted Majority Algorithm, and more broadly the problem of adversarial online learning and regret minimization, and this will launch us into topics such as von Neumanns Minimax Theorem, multi-armed bandit problems, Blackwell Approachability, calibrated forecasting, and proper scoring rules. I intend to spend some time on applications to finance, like repeated gambling, universal portfolio selection, and option pricing.

There will be no specific prerequisites for the course, but the material is going to be about 80% "theory" and thus a strong mathematical background will be important. We shall rely heavily on techniques from calculus, probability, and convex analysis, but most tools will be presented in lecture. There will be a small number of problem sets, and the final project for the course will consist of the option to do independent research or to give a literature review presentation to the class. [Full Story]

Fall 2013: EECS 598-005 Hybrid Systems Control

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: Necmiye Ozay
Prerequisites: EECS 562 or EECS 560 and instructor permission

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 2013: ENGR 390/599 Imagine Innovate Act

Course No.: ENGR 390
Credit Hours: 3 credits
Instructor: Jasprit Singh
Prerequisites:

Course Description:
This course is open to students interested in technology and/or culture and/or wellness. To learn more about the course, contact Prof. Jasprit Singh (singh@umich.edu) or Dr. John Hinckley (ncko@umich.edu). Flyer attached! [More Info]

Fall 2013: EECS 598 Ultra-Low-Power CMOS Circuit Design

Course No.: EECS 598
Credit Hours: Up to 4 credits
Instructor: Pinaki Mazumder
Prerequisites: EECS 312 or EECS 427 or permission of instructor

Course Description:
Following the trajectory of the Moores Law, the integration density of VLSI chips has grown exponentially from two thousand transistors per chip in the early Seventies (i4004) to over one billion transistors (Itanium) in 2009. During this time, CMOS VLSI design has witnessed multiple generations of evolution as the CMOS circuit design focus gradually shifted from Silicon real estate (in the late 70s) to timing closure (in the late 80s), to power aware (in the late 90s), and then to process variations(reliability) at sub-100 nm transistor dimensions. This course envisages studying energy-aware CMOS circuit design techniques that are currently being used in building low-power (at nominal supply voltage) and ultra-low-power (in subthreshold region) VLSI systems. Students interested in taking this course must have basic background in CMOS design (equivalent to EECS 312) and are expected to know circuitequations for minimization of power consumption as well as energy-delay optimization. The course will mainly focus on various aspects of sub-threshold CMOS circuit design as outlined below. [More Info]

Fall 2013: EECS 598 VLSI Digital Signal Processing Systems

Course No.: EECS 598
Credit Hours: 3 credits
Instructor: Zhengya Zhang
Prerequisites: EECS 427 or permission of instructor

Course Description:
Digital signal processing (DSP) systems have been enabled by the advances in very-large scale-integrated (VLSI) technologies. New DSP applications constantly impose new challenges on VLSI implementations. These implementations must satisfy real-time constraints imposed by the applications and must fit increasingly stringent area and power envelope. This course will survey methodologies needed to design efficient and high-performance custom or semi-custom VLSI systems for DSP applications. The primary focus of the course is on design of architectures, algorithms, and circuits, which can be operated with small area and low power consumption to deliver a high speed and functional performance. [More Info]

Fall 2013: EECS 598 Electricity Networks and Markets

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

Course Description:
This course covers the principles and practices that underpin reliable and economical operation of power systems. Power system networks and modeling will be discussed, and an overview of closed-loop controls and basic stability concepts will be provided. System control centres will be considered, primarily in terms of supervisory control and data acquisition (SCADA) and energy management system (EMS) requirements. Power system state estimation will be presented, along with techniques for on-line evaluation of system reliability. The course will investigate optimal generation scheduling and dispatch, including unit commitment, economic dispatch, optimal power flow, and automatic generation control (AGC). Electricity market structures and mechanisms will be presented, with consideration given to the roles of day-ahead and real-time markets, energy and capacity markets, bilateral trading, and markets for ancillary services. The issues that arise from trading over transmission networks will be considered. A comparison of various markets, including MISO, PJM, AEMO, and the failed Californian market will be undertaken. Issues arising from the variability and uncontrollability of renewable generation will be explored. [More Info]

Winter 2013: EECS 598-007: Advanced Topics in Computer Vision

Course No.: EECS 598-007
Credit Hours: 3 credits
Instructor: Silvio Savarese
Prerequisites: EECS 442 or EECS 545 or equivalent

Course Description:
The course surveys recent developments in high level computer vision such as object recognition and categorization, action and event recognition, object tracking and human motion analysis, spatial and temporal reasoning for scene reconstruction and understanding, organization and indexing of visual data from large databases, mobile computer vision. The course also explores advanced classification and inference algorithms for high level visual tasks. [More Info]

Winter 2013: EECS 598-005 Waves and Imaging in Random Media

Course No.: EECS 598-005
Credit Hours: 3 credits
Instructor: John Schotland
Prerequisites: Basic partial differential equations and some knowledge of probability theory

Course Description:
(Course is cross-listed with MATH 651)

This is a special topics course. The focus is on the theory of wave propagation in inhomogeneous 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 diusion theory for high-frequency waves in low-frequency random media. Applications to inverse problems in imaging will be considered. The necessary tools from asymptotic analysis, scattering theory and probability will be developed as needed. The course is meant to be accessible to graduate students in mathematics, physics and engineering. [More Info]

Winter 2013: EECS 598-003 Carbon Nanoelelectronics and Nanophotonics

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Zhaohui Zhong
Prerequisites: EECS 420 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 photonic devices. The course will also have two student labs of testing graphene nanoelectronics. [More Info]

Winter 2013: EECS 598-002: Terahertz Technology & Applications

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Mona Jarrahi
Prerequisites: EECS 320 and (330 or 334)

Course Description:
This course will provide graduate students with an overview on the unique specifications of terahertz waves and potential applications as well as the state of the current terahertz systems and the major technological challenges in the field. The topics covered in this course are THz Detectors (single-photon detectors, microbolometers, Golay cells, Pyroelectric detectors, diode detectors, and focal-plane arrays), THz Sources (vacuum-electronics-based, semiconductor-based, photoconduction-based and nonlinearity-based), THz electronic components (waveguides, Metamaterials, filters and modulators), sensing with THz radiation (THz spectroscopy, imaging and tomography), and THz applications (biology, medicine, space sciences, pharmaceutical industry, security and communications). [More Info]

Winter 2013: EECS 498-004 Grid Integration of Alternative Energy Sources

Course No.: EECS 498-004
Credit Hours: 4 credits
Instructor: Ian Hiskens
Prerequisites: EECS 215 or EECS 314 or permission of instructor

Course Description:
The course will present a variety of alternative energy sources, along with energy processing technologies that are required for power system connection. System integration issues will be addressed, with consideration given to impacts on current power system design philosophies and operating principles. Topics will be covered at a level suited to establishing a broad understanding of the various technologies, and of the associated system implications. [More Info]

Winter 2013: EECS 598-008 Medical Device Security

Course No.: EECS 598-008
Credit Hours: 3 credits
Instructor: Kevin Fu
Prerequisites: Graduate standing or permission of instructor

Course Description:
This course teaches students the engineering concepts and skills for creating more trustworthy software-based medical devices ranging from pacemakers to radiation planning software to mobile medical apps. Topics span computer engineering, human factors, and regulatory policy. Students will master technical skills in reverse engineering, static analysis, fuzz testing, hazard analysis, validation, requirements engineering, radio-frequency communication, physiological sensing, and fundamental concepts from system engineering that lead to safer and more effective medical devices that are increasingly interconnected and wirelessly controlled.

Students will apply the newly learned concepts and skills by analyzing the security of a real-world medical device in a hands-on term project. Interdisciplinary teams will consist of students from complementary backgrounds to mimic the composition of teams at medical device manufacturers and regulatory bodies. Occasional guest speakers from medical device manufacturers, hospitals, and government will complement the classroom activities with critical lessons from the front lines. [Full Story]

Winter 2013: Advanced Signal Processing & Applications

Course No.: 498
Credit Hours: 3
Instructor: Prof. Raj Rao Nadakuditi
Prerequisites: 401, 451

Course Description:
Course No.: 498
Credit Hours: 3
Instructor: Raj Rao Nadakuditi
Prerequisites: EECS 451, EECS 401 or permission of instructor

Course Description:
This is an course on advanced topics in signal processing designed to follow up on principles learned in EECS 451 and EECS 401. The central theme of the course is the application of tools from linear algebra to signal processing. Theoretical topics include solving least-squares problems, eigenvalues and eigenvalues, the singular value decomposition, Markov chains, power method. Synergistic applications covered include image compression, handwriting recognition, Googles PageRank algorithm, eigen-faces, community detection in networks, and deconvolution. Students are expected to be familiar with material covered in EECS 451 and EECS 401 and should have basic MATLAB programming skills (such as writing loops, plotting functions, etc.) [More Info]

Winter 2013: EECS 498-002 Human-Centered Computing

Course No.: EECS 498 - 002
Credit Hours: 3 credits
Instructor: Emily Mower Provost
Prerequisites: EECS 280 or graduate standing

Course Description:
In this course we will cover the techniques that underlie the state of-the-art systems in the human-centered computing field. Students will develop a critical understanding of HCC systems ranging from data collection to human state recognition to feedback. The course evaluation will include homework, a midterm exam, and a final project. [More Info]

Winter 2013: EECS 598-001 Solar Cell Device Physics

Course No.: EECS 589-001
Credit Hours: 3 credits
Instructor: Jamie Phillips
Prerequisites: EECS 421 or graduate standing. Previous knowledge of semiconductor physics is essential for the course.

Course Description:
This course will focus on the physical operation of diode solar cell devices, and detailed analysis of factors that determine the ultimate power conversion efficiency. Topics of study will include internal quantum efficiency of solar cell materials, diode device structures, light management, and current and future solar cell technologies. [More Info]

Winter 2013: EECS 600 Function Space Methods in System Theory

PLEASE NOTE: THIS COURSE IS NOT OFFERED EVERY SEMESTER!

Topics covered will include vector spaces, normed spaces, and Hilbert spaces; the Projection Theorem and complete orthogonal systems; linear operators, bounded operators, adjoint operators; The Hahn-Banach Theorem; the Riez Representation Theorem; Duality. Most of the class will be to learn about results on abstract and possibly infinite-dimensional spaces, but with time we will also explore finite dimensional results specifically for optimization; e.g. Farkas lemma and its role in the proof of KKT Theorem. [More Info]

Fall 2012: EECS 498 Introduction to Machine Learning

Course No.: EECS 498-002
Credit Hours: 3 credits
Instructor: Satinder Singh
Prerequisites: EECS 281 or (EECS 203 and substantial programming experience)

Course Description:
Making sense of data, whether it comes from social settings like Twitter or from scientific experiments in a research laboratory or from NASA observatories, is a problem of great interest to society. Machine learning approaches help us classify, cluster, display, predict, and decide how to act based on data. In this course, we will learn about and program machine learning algorithms and evaluate them on various kinds of data (from twitter feeds, flickr feeds, video, blogs, as well as scientific data).

Fall 2012: EECS 598 Power System Dynamics and Control

Course No.: EECS 598-002
Credit Hours: 3 credits
Instructor: Ian Hiskens
Prerequisites: EECS 463 or instructor permission

Course Description:
Syllabus:

1. Overview of angle and voltage stability concepts, nonlinear dynamical systems, geometrical properties of solutions, Lyapunov methods, bifurcation analysis.

2. Modeling, differential algebraic systems, hybrid dynamical systems.

3. Small disturbance (linear) stability analysis.

4. Large disturbance (nonlinear) analysis, numerical integration, trajectory sensitivities, shooting methods, parameter estimation, grazing, limit cycles.

5. Future grid responsiveness, non-disruptive load control, model predictive control. [More Info]

Fall 2012: EECS 598 Mazumder

Course No.: EECS 598-004
Credit Hours: 3 credits
Instructor: Pinaki Mazumder
Prerequisites: EECS 312 or instructor permission

Course Description:
This course envisages studying energy-aware CMOScircuit design techniques that are currently being used in building low-power (at nominal supply voltage)and ultra-low-power (in subthreshold region) VLSI systems. Students interested in taking this coursemust have basic background in CMOS design (equivalent to EECS 312) and are expected to know circuitequations for minimization of power consumption as well as energy-delay optimization. The course willmainly focus on various aspects of subthreshold CMOS circuit design as outlined below. [More Info]

Fall 2012: EECS 598 FMMs and Integral Equation Solvers

Course No.: EECS 598-003
Credit Hours: 3 credits
Instructor: Eric Michielssen
Prerequisites: EECS 530 or instructor permission

Course Description:
This course focuses on integral equation methods for solving the classical partial differentialequations of mathematical physics along with fast algorithms that effect their iterative solution. Our focuswill be fast (multipole) algorithms for low-rank-under-compression kernels (e.g. Poisson), and Greenfunctions for the Helmholtz and time-dependent wave equations (including their Maxwell equationextensions). Additional topics include FFT and Butterfly-accelerated solvers, and recent developments indirect solvers. At the end of the course, students should be able to comfortably read current fast algorithmliterature and use fast multipole constructs in their own research. [More Info]

Fall 2012: Ultra-Low-Power CMOS Circuit Design

Course No.: 598
Credit Hours: 3
Instructor: Pinaki Mazumder
Prerequisites: EECS 312 or EECS 427 or Instructors consent.

Course Description:
Meeting Pattern: Mon and Wed [More Info]

Fall 2012: EECS 498 - Hands-On Robotics

Course No.: EECS 498
Credit Hours: 4 credits
Instructor: Shai Revzen
Prerequisites: EECS senior standing; other majors and grad students with instructor approval

Course Description:
Engineering seniors and grad students are invited to sign up for Hands On Robotics 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. Grade is 80% team project reports; 20% quizzes. Class meets twice a week for two hours in the lab, with additional lab access provided for working on projects. [More Info]

Fall 2012: EECS 566 Discrete Event Systems

Course No.: EECS 566
Credit Hours: 3 credits
Instructor: Stephane Lafortune
Prerequisites: Graduate standing in EE:Systems, CSE, ME, AERO, or IOE, or senior standing with instructor permission

Course Description:
This course is intended for engineering and computer science graduate students who want to learn about dynamic systems with discrete state spaces and event-driven transitions. Discrete Event Systems, as they are called, arise in the modeling of technological systems such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. In embedded and networked systems, discrete event dynamics are coupled with continuous dynamics, giving rise to what are called Hybrid Systems or Cyber-Physical Systems. This course will introduce students to the modeling, analysis, and control of Discrete Event Systems. The primary emphasis will be on the logical, or untimed, behavior and associated verification and supervisory control problems. Timed Automata and Hybrid Automata will also be introduced. Examples from the above areas will be used throughout the course to illustrate the main concepts. [More Info]

Winter 2012: Ubiquitous Parallelism

Course No.: EECS 598
Credit Hours: 3
Instructor: Satish Narayanasamy
Prerequisites: EECS 470 or 482 or grad standing

Course Description:
Processors with over hundred cores have already become a reality. However, technologies that can allow mainstream programmers to take advantage of this massive parallelism remains to be a grand challenge in computer science. This course will cover recent advances that seek to address this challenge. We will discuss holistic solutions that cut across the computing stack from languages to processor design. Specific topics include high-productivity languages, transactional memory, deterministic parallel computing, GPGPU, MapReduce, multi-core OS, active testing, speculative parallelism, etc.

The course includes a term project. We may be able to get you access to latest parallel programming tools and systems for your project such as compute resources in a cloud, many-core systems, Thread checker, record-n-replay tool, debugging tools such as CHESS, etc. [More Info]

Winter 2012: Autonomous Robotics Laboratory

Course No.: EECS 498
Credit Hours: 4
Instructor: Ed Olson
Prerequisites: EECS 281 or instructor permission

Course Description:
This course will provide students with essential theoreticalbackground and hands-on experience in central topics inrobotics. These include: kinematics, inverse kinematics,sensors and sensor processing, and motion planning. Teamsof students will explore these subjects through a series ofchallenge-themed laboratory exercises. Successful studentswill develop a pragmatic understanding of both theoreticalprinciples and real-world issues, enabling them to designand program robotic systems incorporating sensing,planning, and acting.

We explore these topics from a computer scienceperspective, but we will also cover critical robotics topicsthat are often omitted from computer science curricula.These may include, for example, electrical circuits, controlsystems, Kalman filters, mechanics, and dynamics.Specialized computer science topics such as embeddedsystems programming, real time operating systems, artificialintelligence, etc., may also make appearances. Nobackground is assumed in these areas.

The course is intended for upper-level computer scienceundergraduates, though any one with the appropriatebackground is welcome. [More Info]

Winter 2012: EECS 499-125 Learning Apps for Primary Education

Course No.: 499
Credit Hours: 3
Instructor: Elliot Soloway
Prerequisites:

Course Description:
In this course, students will create apps for Windows Mobile 7 devices that support primary school children (e.g., grade 3) as they learn. For example, we will create math games, science simulations, a timeline app, a storytelling app, etc. These apps will be used by students in Nan Chiau Primary School in Singapore during the 2012 term. There will be an opportunity for some students in the 499 class to go to Singapore to install and test their apps with the Nan Chiau students. Senior standing and excellent software development skills required. Questions? Contact Dr. Elliot Soloway, Soloway@umich.edu [More Info]