New Course Announcements | Electrical & Computer Engineering at Michigan (2024)

Course No:

EECS 498-014/EECS 598-013

Credit Hours:

4 credits

Instructor:

Krisztian Flautner

Prerequisites:

EECS 281

“First comes thought; then organization of that thought, into ideas and plans; then transformation of those plans into reality. The beginning, as you will observe, is in your imagination.” – wrote Napoleon Hill, an entrepreneur, author of self-help books, and a conman, in the early 20th century. His point was that innovation requires a certain action-oriented but critical mindset for success and that starting on that journey can often be the hardest part. Whether trying to innovate inside an organization, working on an open source project, or attempting to get a startup off the ground, the tools and techniques discussed in this class will be applicable to increase the chances of success. Students will be expected to propose and critically evaluate project ideas, form groups, and execute autonomously to achieve objectives. The groups will report directly to the “general manager” (faculty), with biweekly project meetings.

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Course No:

EECS 398-001

Credit Hours:

4 credits

Instructor:

L. Jay Guo and Zheshen Zhang

Prerequisites:

An introductory optics course like EECS 334 or similar

This course will provide the students with the foundation knowledge to understand the development of this rapidly evolving field, leading to the discussion of new technologies. We will address how the mysterious quantum phenomena are brought to real world realizations that will further advance our knowledge. After introducing the founding principles of quantum science and quantum information, the second half of the semester will focus on photonic realization because of its appeal in delivering near-term quantum technologies that would create far-reaching societal impacts. By the end of the course, the students will grasp a fundamental understanding for quantum information and be able to bridge quantum physical phenomena and new technologies for communication, sensing, and computing.

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Course No:

EECS 298-001

Credit Hours:

4 credits

Instructor:

Benjamin Fish

Prerequisites:

EECS 280 or permission of instructor

This class will introduce you to the ways in which applications of computing affect social institutions and how these social consequences produce questions about how to conceptualize, critique, and ensure our all-too-human values in computing.

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Course No:

EECS 598-004

Credit Hours:

4 credits

Instructor:

Mosharaf Chowdhury

Prerequisites:

At least one of EECS 482, EECS 484, EECS 491, or EECS 489

This class will introduce you to the key concepts and the state-of-the-art in practical, scalable, and fault-tolerant software systems for emerging Generative AI (GenAI) and encourage you to think about either building new tools or how to apply an existing one in your own research.

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Course No:

EECS 598-014

Credit Hours:

3 credits

Instructor:

Wei Hu

Prerequisites:

Familiarity with probability, multivariate calculus, and linear algebra is required

This course will study the theoretical foundations of machine learning. We will present the frameworks and rigorously analyze some of the most successful algorithms in machine learning that are extensively used. The course will prepare students for thinking rigorously about machine learning and doing research in a relevant area.

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Course No:

EECS 598-012

Credit Hours:

3 credits

Instructor:

Mackillo Kira

Prerequisites:

EECS 428 or 540 or equivalent

This lecture will provide a pragmatic and brief introduction to solid-state theory, many-body formalism, semiconductor quantum optics, and lightwave electronics to explore pragmatic possibilities for quantum technology.

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Course No:

EECS 598-003

Credit Hours:

3 credits

Instructor:

Raj Nadakuditi

Prerequisites:

EECS 551, programming experience, linear algebra

his course explores the theoretical and practical limitations of machine learning algorithms, such as computational complexity, data quality and quantity. The course covers both classical and modern results in algorithmic learning theory, as well as recent advances and challenges in deep learning. The course also discusses the implications of these limitations for the design and deployment of machine learning systems in various domains.

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Course No:

EECS 598-001

Credit Hours:

3 credits

Instructor:

Mark Kushner

Prerequisites:

Prior coursework in plasmas or permission of instructor

This course addresses the plasma initiated chemistry and plasma surface interactions of these systems.

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Course No:

EECS 498-018 / EECS 598-018

Credit Hours:

4 credits

Instructor:

Ali Movaghar

Prerequisites:

EECS 281 or EECS 403 or graduate standing

See link below for more information.

More info (pdf)

Course No:

EECS 498-016 / EECS 598-016

Credit Hours:

4 credits

Instructor:

Samet Oymak

Prerequisites:

EECS 445 or 453 or 505 or 545 or 553

See link below for more information.

More info (pdf)

Course No:

EECS 498-015 / EECS 598-015

Credit Hours:

4 credits

Instructor:

Jiasi Chen

Prerequisites:

EECS 280

See link below for more information.

More info (pdf)

Course No:

EECS 498-010

Credit Hours:

4 credits

Instructor:

Stephen Forrest

Prerequisites:

EECS 230 and EECS 320

This course is focused on describing the technologies available or envisioned that can replace fossil fuels with alternative renewable sources of energy.

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Course No:

EECS 498-009

Credit Hours:

4 credits

Instructor:

Manos Kapritsos

Prerequisites:

None

During this course, you will learn how to formally specify a system’s behavior, how to prove that the high-level design of the system meets that specification and finally how to show that the system’s low-level implementation retains those properties. The course does not assume any prior knowledge in formal verification. We will start from the basics of the Dafny language and build from there. In the end, you should be able to design and prove correct a complex system. The experience of doing so will make you a more careful and effective programmer, even when you don’t write formally verified code.

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Course No:

EECS 498-006/EECS 598-006

Credit Hours:

4 credits

Instructor:

Gokul Subramanian Ravi

Prerequisites:

EECS 370 or permission of instructor

This course will primarily focus on learning and research at the intersection of quantum and classical computing.

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Course No:

EECS 498-005

Credit Hours:

4 credits

Instructor:

Michal Derezinski

Prerequisites:

EECS 376 (advisory), linear algebra and probability

The course will cover several im-portant algorithms in data science and demonstrate how their performances can be analyzed. While fun-damental ideas covered in EECS 376 (e.g., design and analysis of algorithms) will be important, some topics will introduce new concepts and ideas, includ-ing randomized dimensionality reduction, sketching algorithms, and optimization algorithms (e.g., for training machine learning models).

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Course No:

EECS 498-004

Credit Hours:

3 credits

Instructor:

Alexander Burgers

Prerequisites:

PHYSICS 240, MATH 215, and MATH 216

This course will introduce students to the quantum theory of electromagnetic radiation, matter and their interactions, which underpins all new quantum technologies.

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Course No:

EECS 498-001

Credit Hours:

4 credits

Instructor:

Jonathan Beaumont

Prerequisites:

EECS 203, EECS 281, EECS 370

Quantum computing, should current technical barriers be overcome, makes bold promises to revolutionize key applications including cryptography, machine learning, and computational physics. This course will explore the potential impact and limitations of this paradigm shift from a computer science perspective. Lectures will cover the bare physics and mathematics needed to investigate how each layer of the computing stack (logic, system architecture, algorithm, and application design) is impacted. Labs and programming assignments will provide students a hands-on approach towards writing quantum programs, simulating their execution, deploying them to real quantum hardware available on the cloud, and analyzing their performance.

More info (pdf)