Programming paradigms including group communication, RPC, distributed shared memory, and distributed objects. (3 credits) Instruction Mode: Online – Synchronous It also covers the optimization methods required to solve planning and operational problems including economic dispatch, optimal power flow, and unit commitment. CourseProfile (ATLAS), EECS 398. (4 credits) Advanced micro electro mechanical systems (MEMS) devices and technologies. All EECS courses at the University of Michigan (U of M) in Ann Arbor, Michigan. Prerequisite: graduate standing (3 credits) Networking. Gaussian beams and ABCD law. Query languages such as SQL, forms, embedded SQL, and application development tools. Fluency in a standard object-oriented programming language is assumed. CourseProfile (ATLAS), EECS 464 (ROB 464). Instruction Mode: Online – Synchronous Advised Prerequisites: EECS 281 and (MATH 214 or 217 or 296 or 417 or 419) and (STATS 412 or MATH 425), or graduate standing. Laboratory techniques for plasma ionization and diagnosis relevant to plasma processing, propulsion, vacuum electronics, and fusion. CMOS circuit delay and power analysis. Wide-issue processors, in-order vs. out-of-order execution, instruction retirement. Knowledge-Based Systems (4 credits) Applications such as image deblurring, ranking of webpages, image segmentation and compression, social networks, circuit analysis, recommender systems and handwritten digit recognition. Theory of digital modulation and coding. Construction and design of lasers; gaussian beams; nonlinear optics; fiber optics; detectors; dispersion; Fourier optics; spectroscopy. Prerequisite: EECS 492. Analysis of time and space utilization. Prerequisite: EECS 562 or MECHENG 548. CourseProfile (ATLAS), EECS 409. We encounter it in our everyday lives in the form of E-mail, newspapers, television, the Web, and even in conversations with each other. Project modules include measurement or respiratory volume and flow rates, biopotentials (electrocardiogram), and optical analysis of arterial blood oxygen saturation (pulse-oximetry). Prerequisite: Graduate Standing or permission of instructor. (to be arranged) (1-4 credits) Introduction to Logic Design Students will build a working compiler for a high-level programming language. CourseProfile (ATLAS), EECS 500. CourseProfile (ATLAS), EECS 586. (3 credits) CourseProfile (ATLAS), EECS 438. (3 credits) Instruction Mode: Online – Asynchronous Introduction to computational methods for identifying patterns and outliers in large data sets. CourseProfile (ATLAS), EECS 490. Principles of Optics Special topics such as femto-seconds lasers and ultrahigh power lasers. CourseProfile (ATLAS), EECS 510. Development of a complete integrated microsystem, from functional definition to final test. Minimum grade of “C” required for enforced prerequisites. Basic concepts of probability theory. (3 credits) View a historical document of past teaching assignments for EECS classes. Theory and applications of probabilistic techniques for autonomous mobile robotics. Computer Vision Instruction Mode: Online – Synchronous The first part of the course focuses on the rules and codes that govern patent prosecution, and the second part focuses on claim drafting and amendment writing. Prerequisite: EECS 330. Instruction Mode: Online – Synchronous Design principles for multidisciplinary team projects, team strategies, entrepreneurial skills, ethics, social and environmental awareness, and life long learning. Directed Study (4 credits). Digital Signal Processing Design Laboratory Weekly recitations and hardware/Matlab software laboratories. The second half treats photons in semiconductors, including semi-conductor lasers, detectors and noise effects. (3 credits) Theoretical treatment and concrete examples, e.g., feature learning, segmentation image stitching, both covered. Topics include semantics, type systems, program verification using theorem provers, software model checking, and program analysis. Prerequisite: graduate standing or permission of instructor. Overview of modern optics with laboratory demonstrations. CourseProfile (ATLAS), EECS 820. CourseProfile (ATLAS), EECS 458 (BIOMEDE 458). DC machines. Students design and construct functioning biomedical instruments. (4 credits) Classical Optics CourseProfile (ATLAS), EECS 583. (3 credits) Theory will cover: Bandstructure in quantum wells; effect of strain on bandstructure; transport theory; Monte Carlo methods for high field transport; excitons, optical absorption, luminescence and gain. CourseProfile (ATLAS), EECS 501. Computability, undecidability, and logic. *For more information regarding course equivalencies please refer to the Course Equivalency section, under “How to Read a Course Description“, in the CoE Bulletin Website:, EECS 101. (3 credits) CourseProfile (ATLAS), EECS 561 (MECHENG 561). Language and Information Greater emphasis on applications than in EECS 551. Advisory Prerequisites: A prior >= 400-level course on computer system or sensor design and analysis. Instruction Mode: Online – Synchronous (4 credits) Minimum grade of “C” required for enforced prerequisites. Power converter topologies. Oversampling converters are also discussed. Prerequisite: MATH 214 or MATH 216, PHYSICS 240. Credit for only one: EECS 215, or EECS 314. CourseProfile (ATLAS), EECS 550. Special Topics  Transduction mechanisms and modeling in different energy domains. Prerequisite: MATH 215, PHYS 240 (or 260), EECS 215. Prerequisite: Enforced: EECS 301 or MATH 425 or STATS 425 or STATS 412 or STATS 426 or IOE 265 or equivalent. Advised Prerequisite: EECS 501; Corequisite:  EECS 453 or EECS 551. Prerequisite: EECS 413 or permission of instructor. CourseProfile (ATLAS), EECS 311. Survey of ultrafast high field interactions. Eligibility is limited to students who have a concentration GPA of 3.5 or better. CourseProfile (ATLAS), EECS 538 (APPPHYS 550) (PHYSICS 650). Maxwell’s equations in differential form. Laboratory exercises using two state-of-the-art fixed-point processors:  A/D and D/A conversion, digital waveform generators, real-time FIR and IIR filters. Prerequisite: None. CourseProfile (ATLAS), EECS 503. Instruction Mode: Online – Synchronous The theory of channel coding for reliable communication and computer memories. An attempt includes, but is not limited to, a notation of any letter grade (A-F), withdraw (W), pass/fail (P/F), transfer (T), or incomplete (I) posted on the U-M transcript. Special Topics in Computer Architecture Instruction Mode: Directed Study Instruction Mode: Online – Synchronous Course discusses advanced topics and research issues in operating systems. Instruction Mode: Online – Asynchronous Topics of current interest selected by the faculty. Basic concepts such as speedup, load balancing, latency, system taxonomies. Datapath and control for multiple implementations of a processor. (3 credits) Prerequisite: graduate standing. Prerequisite: EECS 281 and graduate standing. Multiple team projects, culminating in a major design experience (MDE) project. Minimum grade of C required for enforced prerequisites. Prerequisite: EECS 429. Optimization Methods in Signal Processing and Machine Learning  (3 credits) Enforced Prerequisites: SI 507 or SI 507 Waiver or SI 508 or CSE Grad Standing. CourseProfile (ATLAS), EECS 650. Semiconductor Optoelectronic Devices Latency tolerance techniques. Lecture, seminar, or laboratory. A hands-on, project based introduction to the principles of robotics and robot design. Advised Prerequisite: Minimum GPA of 2.5 over the best grade for each enforced prerequisite. Digital System Testing Computer Networks Practical design work is a significant part of this course. Reciprocity. Analysis of circuit delay, power, and noise margins. Wire antennas: dipoles, loops and traveling-wave antennas. Fundamentals of electromagnetic radiation and propagation (near earth, troposphere, ionosphere, indoor and urban); antenna parameters; practical antennas; link analysis; system noise; fading and multipath interference; applications. Also, student groups work on reviewing business books, case studies, elevator and investor pitches. (3 credits) Instruction Mode: Hybrid – Synchronous, Online – Asynchronous Prerequisite: EECS 280 and (EECS 203 or Math 465 or Math 565). Prerequisite: (EECS 203 or Math 465 or Math 565) and EECS 280. Practical Programming in Java Theory and application of matrix algorithms to signal processing, data analysis and machine learning. Optical information processing, including spatial matched filtering. CourseProfile (ATLAS), EECS 493. CourseProfile (ATLAS), EECS 574. Flat Panel Displays Specific topics vary each time the course is offered. Prerequisite: EECS 330 (“C” or better) or graduate standing. Fundamental concepts and methods in data mining, and practical skills for mining massive, real data on distributed frameworks (e.g., Hadoop). Fundamental concepts in programming languages. (2 credits) Prerequisite: Enforced: EECS 281. Prerequisite: EECS 414. Instruction Mode: Online – Synchronous Readings assigned from research publications. CMOS logic circuit families, adders, multipliers, memory arrays, sequential circuits, and interconnects. Emphasizes construction of systems using graphics application programming interfaces (APIs) and analysis tools. Instruction Mode: Online – Synchronous Concepts and techniques for designing computer system user interfaces to be easy to learn and use, with an introduction to their implementation. (4 credits) umich eecs grad course list provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. (4 credits) Coursework comprises extensive reading, research and writing assignments, presentations, quizzes, and the replication project. While the target audience is EE/CE/CS/DS students, any student wishing to learn how to use their computer more effectively is encouraged to join. Instruction Mode: In Person – Synchronous Applications include signal denoising, compressed sensing, matrix completion, robust regression, and classifier design. All of these courses are geared toward different audiences, have different prerequisites, and satisfy different program requirements. • Email us at Apply electrical engineering concepts in circuits, computing, control, sensors, optics, power, signal processing, and wireless communications to a system such as a robot, and adapt the system to achieve competition objectives within defined engineering constraints. Magnetosphere and Solar Wind Fundamentals of the theory of computation and complexity theory. The course will teach concepts and present case studies through lectures, homework, design problems, and a final project. Dielectric waveguides. CourseProfile (ATLAS), EECS 429. Advanced Computer Graphics The defense of the dissertation, that is, the final oral examination, must be held under a full-term candidacy enrollment. Introduction to Computer Security Self-testing circuits and systems. (4 credits) Basic principles of optics: light sources and propagation of light; geometrical optics, lenses and imaging; ray tracing and lens aberrations; interference of light waves, coherent and incoherent light beams; Fresnel and Fraunhofer diffraction. Data management for emerging application areas, including bioinformatics, the internet, OLAP and data mining. Instruction Mode: Online – Synchronous (3 credits) topics include: cameras models, the geometry of multiple views; shape reconstruction methods from visual cues: stereo, shading, shadow, contours; low-level image processing methodologies such as edge detection, feature detection; mid-level vision techniques (segmentation and clustering); Basic high-level vision problems: face detection, object and scene recognition, object categorization, and human tracking. Technologies such as electronic commerce, social media, and satisfy different program requirements [ Fewer than previous. Under the supervision of a processor of data compression Prerequisite: EECS or. Course requirements, topics, and managing secure systems includes informative labs a..., sliding modes and feedback linearization a novel visual programming environment a novel visual environment... 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