New Course Announcements

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. 

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.

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.

 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. 

 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. 

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.

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.

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.

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.

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.

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.

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

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

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

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.

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.

Please see flyer.

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.

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.

• 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)

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.

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.