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New Course Announcements

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Fall 2023: Causality and Machine Learning

Course No:
EECS 598-009
Credit Hours:
3 credits
Instructor:
Maggie Makar
Prerequisites:
Familiarity with statistics, probability and machine learning. Knowledge of python.

This course introduces the fundamental concepts of causality, and causal inference using machine learning models. Topics will include:counterfactuals (potential outcomes and graphs), identification and estimation of conditional average treatment effects from randomized control trials and observational data, as well as causal inference under hidden confounding and limited overlap. 

More info (pdf)

Fall 2023: Theory of Network Design

Course No:
EECS 598-003
Credit Hours:
3 credits
Instructor:
Greg Bodwin
Prerequisites:
EECS 376 with a B+ or better, graduate standing or permission of instructor

This is a proof-based course that lies at the intersection of algorithms and graph theory. We will tour through some classic algorithms and cutting-edge work in the area of network design. Topics will include distance oracles, spanners, emulators, preservers, shortcut sets, hopsets, algorithmic applications of these objects, and methods for making these objects tolerant to temporary failures in a network.

More info (pdf)

Fall 2023: Quantum Computing, Information and Probability

Course No:
EECS 598-005
Credit Hours:
3 credits
Instructor:
Sandeep Pradhan
Prerequisites:
Undergraduate linear algebra and probability

The aim of the course is to develop the key concepts of quantum computing and information as well provide hands-on quantum programming skills (Qiskit platform). A basic working knowledge of linear algebra is a prerequisite, but no prior knowledge of quantum mechanics, classical computing or information theory is assumed. Graduate students in all areas of engineering, computer science, system theory, the physical sciences and mathematics should find this material of interest.

More info (pdf)

Fall 2023: Machine Learning Basics for Optics & Photonics

Course No:
EECS 498-004
Credit Hours:
3 credits
Instructor:
Mohammed Islam
Prerequisites:
None

 AI is transforming many industries and has caused an explosion of applications. Areas that have been affected by ML and deep learning include self-driving cars, speech and image recognition, effective web searching, fraud detection, human genome analysis, and many other advances. Knowledge of AI, ML and Deep Learning is becoming a must for any engineer or scientist. This course is intended to give you exposure to the underlying theory and language. This is an introduction for non-experts, and it will enable you to go onto other AI, ML and deep learning courses offered in various departments. 

More info (pdf)

Fall 2023: Extended Reality and Society

Course No:
EECS 498-003
Credit Hours:
4 credits
Instructor:
Austin Yarger
Prerequisites:
EECS 281

Fall 2023: From 51 Billion to Zero: Challenges and Opportunities in Reducing Greenhouse Gas Emissions

Course No:
EECS 298
Credit Hours:
1 credit
Instructor:
Stephane Lafortune
Prerequisites:
None

 EECS 298-051, Fall 2023, will be a seminar-type course with presentations by the instructor and invited speakers. The goal of this course is two-fold. First, the understanding of how human activities, from electricity generation to transportation, construction, agriculture, and heating/cooling, contribute to the release of greenhouse gases (GHG) in the Earth’s atmosphere. Second, the study of current and prospective engineering solutions for reducing and potentially eliminating GHG emissions, with several presentations by UM experts. 

More info (pdf)

Winter 2023: Machine Learning Theory

Course No:
EECS 598-014
Credit Hours:
3
Instructor:
Wei Hu
Prerequisites:
Mathematical maturity. Familiarity with probability, multivariate calculus, and linear algebra is required. Knowledge of machine learning is recommended but not required.

When do machine learning algorithms work and why? How do we formally characterize what it means to learn from data? This course will study the theoretical foundations of machine learning. Tentative topics include generalization, optimization, deep learning, online learning and bandits, and unsupervised learning.

 

More info

Winter 2023: Applied Machine Learning for Modeling Human Behavior

Course No:
EECS 448
Credit Hours:
4
Instructor:
Emily Provost
Prerequisites:
“Enforced Prerequisite: EECS 281 and (MATH 214 or 217 or 296 or 417 or 419, or ROB 101); (C or better; No OP/F) or Graduate Standing in CSE Advisory Prerequisite: EECS 445”

Machine learning, with a focus on human behavior, across multiple modalities including speech and text. Teams complete projects based primarily on their individual interests centered on modeling an aspect of human behavior. Prior experience with speech/language or other data modeling is not needed.

More info

Winter 2023: CSE Seminar

Course No:
EECS 598-007
Credit Hours:
1
Instructor:
Nikhil Bansal and Satish Narayanasamy
Prerequisites:
Graduate standing

Winter 2023: Data Centric Systems

Course No:
EECS 598-013
Credit Hours:
3
Instructor:
Reetuparna Das
Prerequisites:
EECS 281, EECS 370, or graduate standing

Winter 2023: Human-AI Interaction & Systems

Course No:
EECS 598-003
Credit Hours:
3
Instructor:
Anhong Guo
Prerequisites:
Graduate standing; or permission from instructor

Human intelligence and artificial intelligence (AI) are intertwined, co-evolving and complementary. This course explores how to combine the complementary strengths of humans and AI to design intelligent interactive systems that are ethical, usable, and useful. We will discuss topics including ways to facilitate humans to interact with AI, systems that combine human and AI to solve complex challenges, crowdsourcing and human computation, explainable AI, and AI fairness and auditing.

More info

Winter 2023: Introduction to the Social Consequences of Computing

Course No:
EECS 298-001
Credit Hours:
4
Instructor:
Benjamin Fish
Prerequisites:
EECS 280 or permission of instructor

Computing is now used in every facet of life affecting countless people, including making policy decisions about people in lending, policing, criminal justice, admissions, advertising, and hiring. In doing so, the process of computing and algorithm design now involves understanding the role of computing in society.
This class will introduce you to the ways in which applications of computing affect societal institutions and how these social consequences produce questions about how to conceptualize, critique, and ensure our all-too-human values in computing. To accomplish this, we will explore computing, particularly artificial intelligence (AI) and machine learning, including exploring the role of AI in everything from personalization to surveillance to online speech. We will critically examine the philosophical and sociological underpinnings of these values and the strategies commonly used to promote them, and seek to connect these conceptualizations to the emerging algorithmic tools proposed for promoting those values. In order to practice reasoning through these problems, this class will feature programming in Python. No previous programming experience in Python is needed.

More info

Winter 2023: Action and Perception

Course No:
EECS 598-010
Credit Hours:
3
Instructor:
Stella Yu
Prerequisites:
Basic knowledge of machine learning, computer vision, and robotics.

In this graduate-level seminar course, we will study research papers on the development of visual, audio, and tactile perception and body control in humans, in comparison with the latest machine learning methods in computer vision and robotics, on tasks such as ocular motor control, reaching, grasping, manipulation, locomotion etc.

More info

Winter 2023: Extended Reality and Society

Course No:
EECS 498-003
Credit Hours:
4
Instructor:
Austin Yarger
Prerequisites:
EECS 281

From pediatric medical care, advanced manufacturing, and commerce to film analysis, first-responder training, and unconscious bias training, the fledgling, immersive field of extended reality may take us far beyond the realm of traditional video games and entertainment, and into the realm of diverse social impact.

“EECS 498 : Extended Reality and Society” is a programming-intensive senior capstone / MDE course that empowers students with the knowledge and experience to…

– Implement medium-sized virtual and augmented reality experiences using industry-standard techniques and technologies (unity / unreal).
– Design socially-conscious, empowering user experiences that engage diverse audiences.
– Contribute to cultural discourse on the hopes, concerns, and implications of an XR-oriented future.
– Carry out user testing and employ feedback in an iterative design / development process.
– Work efficiently in teams of 2-4 using agile production methods and software (Jira)

Students will conclude the course with at least three significant, socially-focused XR projects in their public portfolios.

More info

Winter 2023: Privacy Enhancing Technologies (PETS)

Course No:
EECS 598-009
Credit Hours:
3 credits
Instructor:
Todd Austin
Prerequisites:
Graduate standing in CSE

This course explores the latest advances in privacy-enhancing technologies (PETs).

More info (pdf)

Winter 2023: Data Centric Systems

Course No:
EECS 598-013
Credit Hours:
3 credits
Instructor:
Reetuparna Das
Prerequisites:
EECS 281, EECS 370 or graduate standing

This special topics course will discuss recent advances and new directions that are being pursued to design data-centric computing systems.

More info (pdf)

Winter 2023: Quantum Electromagnetics

Course No:
EECS 498-004
Credit Hours:
3 credits
Instructor:
Alex Burger
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.

More info (pdf)

Winter 2023: Algorithms for Data Science

Course No:
EECS 498-005
Credit Hours:
4
Instructor:
Michal Derezinski
Prerequisites:
EECS 376, linear algebra and probability

This course will introduce algorithmic and theoretical aspects of data science. With the emergence of machine learning and data science, as well as the ever-increasing data sizes, providing theoretical foundations for these areas will become increasingly important. The course will cover several important algorithms in data science and see how their performances can be analyzed. While fundamental ideas covered in EECS 376 (e.g., design and analysis of algorithms) will be still important, some topics will introduce new concepts and ideas, including randomized dimensionality reduction, sketching algorithms, and algorithms for continuous optimization.

More info

Winter 2023: Formal Verification of Hardware and Software

Course No:
EECS 598-002
Credit Hours:
4 credits
Instructor:
Karem Sakallah
Prerequisites:
Graduate standing in CSE

This course explores the latest advances in automated proof methods for checking whether or not certain properties hold under all possible executions of a complex hardware or software system. Specifically, we focus on the class of “control-centric” properties, namely those properties that are weakly-dependent on the data state of the system.

More info (pdf)

Fall 2022: Machine Learning Basics for Optics and Photonics

Course No:
EECS 498-014
Credit Hours:
2 credits
Instructor:
Mohammed Islam
Prerequisites:
See instructor

Please see flyer.

More info (pdf)

Fall 2022: ENGR 490 Designing Your Engineering Future

Course No:
ENGR 490-005
Credit Hours:
2 credits
Instructor:
Joanna Millunchick and Mike Dailey
Prerequisites:
At least one experiential (i.e., active, concrete, contextual) experience

ENGR 490.004 and 490.005 meet together for the first seven weeks of the semester. Then, ENGR 490.005 continues to the end of the semester. * Indicates information specific to ENGR 490.005.

As graduation approaches, you have engaged in a wealth of experiences and collected a bounty of stories. As you move forward to new experiences, you may have many questions about your future: What career do I want? What lifestyle? What jobs should I apply for? Accept? Should I attend graduate school? Am I an effective engineer?

This course will help you leverage your past experiences to create and use tools that will help you answer questions about your personal and professional futures. You’ll create a set of guiding principles and a professional statement and begin a vision for your future. You’ll then apply your principles and vision to make challenging decisions and create professional documents that will be useful in your near future. Throughout this course, you’ll use a set of competencies and collaborate with a group of peers and mentors from academia and industry alike.

* Then, you’ll develop and apply a project to meet your personal and professional goals. Examples of projects include a website, a LinkedIn profile, a vision, or a portfolio. You’ll further examine competencies, such as ethical reasoning, and apply them to examples that engineers often experience at work.

More info

Fall 2022: Formal Verification of Systems Software

Course No:
EECS 498-008
Credit Hours:
4 credits
Instructor:
Manos Kapritsos
Prerequisites:
EECS 491

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.

More info (pdf)

Fall 2022: Power Semiconductor Devices

Course No:
EECS 598-005
Credit Hours:
4 credits
Instructor:
Becky Peterson
Prerequisites:
EECS 320 and EECS 421 or graduate standing
  • Learn how power switches (transistors) and rectifiers (diodes) work
  • Gain familiarity with the materials used for power devices
  • Understand how device design determines performance
  • Learn how to use commercial software to numerically model power devices through guided projects (Synopsys Sentaurus and Silvaco Atlas)
More info (pdf)

Fall 2022: Science of Deep Learning

Course No:
EECS 598-007
Credit Hours:
3 credits
Instructor:
Wei Hu
Prerequisites:
Knowledge of machine learning (EECS 545 or 445 or equivalent) and mathematical maturity

This is a graduate-level research-oriented course focusing on fundamental principles (“science”) of deep learning, from both theoretical and empirical perspectives. We will aim to cover fundamental theory, ideas, phenomena, and challenges underlying recent advances in deep learning. Deep learning is a fast-evolving field, and anything can change at any moment, so it’s good to keep an open and critical mindset. If you see something that seems unsatisfactory, you are probably right and you should try to understand it better and improve it!

Note that the focus of this course is on the theoretical and scientific understanding of deep learning. It will not teach you how to use deep learning packages. It is also not about the applications of deep learning to scientific domains.

 

More info

Fall 2022: Quantum Computing for the Computer Scientist

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)
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