Electrical and Computer Engineering
Home > Academics > Course Information > Course Descriptions > New Course Announcements

New Course Announcements

Winter 2021: Adversarial Machine Learning

Course No:
EECS 598-007
Credit Hours:
3 credits
Instructor:
Atul Prakash
Prerequisites:
Grad standing or permission of instructor; familiarity with machine learning (e.g. EECS 445)

This is a new special topics course that will look at recent advances in the field of adversarial machine learning, both from an attack and defense perspective.  Deep neural networks (DNNs) are widely used in computer vision for both detecting and classifying objects and are relevant to emerging systems for autonomous driving. Unfortunately, there is a question of  trust,  are machine learning (ML)  models sufficiently robust to make correct decisions when human safety is at risk? This course will examine research papers in this field looking at vulnerabilities or defenses in machine learning systems with respect to various types of attacks including data poisoning attacks during training time or during online learning, data perturbation attacks on a trained model to cause misclassifications, and deepfake attacks.  Papers on bias and fairness in machine learning systems are also within scope. 

The class will be conducted seminar style and involve presentations by students, discussions, and projects to help everyone in the class up to speed on the foundations and cutting-edge research in the field. Each  group will be expected to share a summary of one attack paper and one defense paper and present the paper to the class during the semester. The group should attempt to reproduce a subset of the results of the paper being presented (or in the rare case that is not possible due to lack of datasets, sufficient detail, lack of computational resources, or models, another paper that is presented in the class).

More info

Winter 2021: GaN-Based Electronic Devices

Course No:
EECS 598-004
Credit Hours:
3 credits
Instructor:
Elaheh Ahmadi
Prerequisites:
EECS 320 or permission of instructor

Device performances are driven by new materials, scaling, and new device concepts such as bandstructure and polarization engineering. Semiconductor devices have mostly relied on Si but increasingly GaAs, InGaAs and heterostructures made from Si/SiGe, GaAs/AlGaAs etc have become important. Over the last few years one of the most exciting new entries has been the GaN based devices that provide new possibilities for lighting, displays and wireless communications. New physics based on polar charges and polar interfaces has become important as a result of the nitrides. For students to be able to participate in this and other exciting arena, a broad understanding of physics, materials properties and device concepts is required. 

More info (pdf)

Winter 2021: Motion Planning

Course No:
EECS 598-006
Credit Hours:
3 credits
Instructor:
Dmitry Berenson
Prerequisites:
Linear algebra (e.g. MATH 214) and significant programming (e.g. EECS 281)

Winter 2021: Quantum Information, Probability and Computation

Course No:
EECS 598-005
Credit Hours:
3 credits
Instructor:
Sandeep Pradhan
Prerequisites:
Permission of instructor

Winter 2021: Technologies to Optimize Human Learning

Course No:
EECS 598-011
Credit Hours:
3 credits
Instructor:
Xu Wang
Prerequisites:
Grad standing or permission of instructor

The advances in computing have changed the ways people learn. In this seminar, we will review educational technologies that draw a wide range of techniques from Augmented Reality, Computer Vision, Natural Language Processing, Crowdsourcing, etc. We will also discuss how these systems are guided by theories of how humans learn and the HCI methods used to design and evaluate them.

More info (pdf)

Winter 2021: Introduction to Natural Language Processing

Course No:
EECS 498-004
Credit Hours:
4 credits
Instructor:
Lu Wang

This course aims to introduce fundamental tasks in natural language processing, and its recent advances based on machine learning algorithms (e.g., neural networks) and applications for interdisciplinary subjects (e.g., computational social science). The course materials are mostly delivered as lectures, and accompanied with reading materials.

More info (pdf)

Winter 2021: Applied Machine Learning for Affective Computing

Course No:
EECS 598-010
Credit Hours:
4 credits
Instructor:
Emily Mower Provost
Prerequisites:
Grad Standing – enrollment is by override only; to request an override please waitlist for the course

This course covers the concepts and techniques that underlie machine learning of human behavior across multiple interaction modalities. Topics include: speech/text/gestural behavior recognition through applications of machine learning, including deep learning. The course will also include discussions of the cybersecurity challenges associated with this domain. Fluency in a standard object-oriented programming language is assumed. Prior experience with speech or other data modeling is neither required nor assumed.

More info (pdf)

Winter 2021: Statistical Learning Theory

Course No:
EECS 598-008
Credit Hours:
3 credits
Instructor:
Clayton Scott
Prerequisites:
EECS 501 and EECS 545 (advisory)

This course will cover statistical learning theory including the following topics: concentration inequalities, consistency of learning algorithms, Vapnik-Chervonenkis theory and Rademacher complexity, reproducing kernel Hilbert spaces and kernel methods, surrogate losses, and deep learning. Unsupervised and online learning may also be covered as time permits.

Students are expected to have (1) a strong background in probability at the level of EECS 501, (2) prior exposure to machine learning algorithms, such as EECS 545, Stat 601, or Stat 605, and (3) some experience with writing formal mathematical proofs as might be acquired in an upper level undergraduate mathematics course.

Grading will be based on occasional homework assignments and an individual end-of-semester report on a topic of the student’s choosing. There may also be a participation component to the grade. There will be no exams.

One desired outcome for students taking this course is an ability to read research articles in the field of machine learning and appreciate the significance of the theoretical performance guarantees describe in those articles. Students developing new algorithms as part of their research can also expect to learn techniques that will help them analyze their algorithms.

More info (pdf)

Winter 2021: Software Defined Radio

Course No:
EECS 398-002
Credit Hours:
4 credits
Instructor:
Achilleas Anastasopoulous
Prerequisites:
EECS 216 or permission of instructor

In this class you will learn basic concepts of software defined radio. You will learn the following:

How basic radios work
A: Upconversion and down conversion
B: Frequency and phase synchronization
C: Timing synchronization
D: Digital modulation and demodulation including BPSK, QPSK, QAM, FSK, OFDM (several of these are used in 5G cellular networks and WiFi).
E: How to implement in software the different modulation and demodulation schemes. Most of what we do is done via a graphical user interface (GUI) but some custom operation can be programmed using Python. No knowledge of Python is assumed.

More info (pdf)

Winter 2021: Succinct Graph Data Structures

Course No:
EECS 598-003
Credit Hours:
3 credits
Instructor:
Gregory Bodwin
Prerequisites:
EECS 203 and EECS 281 and MATH 217 (or equivalent), grad standing, or permission of instructor

From social networks to road maps to the internet, the modern world is dominated by data represented in enormously large graphs. An effective way to make sense of a massive graph is to create a much smaller one that is “similar” to the original in some critical ways. But how accurately can this be done? What methods of graph compression take on the least error? What structural properties of a network make it easy or hard to accurately express in small space? This class will develop the theoretical underpinnings of graph compression algorithms in a mathematically rigorous, proof-based way.

Topics will include graph spanners and distance preservers (which compress graph distances), block models and regularity lemmas (which compress graph cuts), and spectral sparsiers (which compress graph spectra). Other topics may be set dynamically, based on active student interest and feedback.
More info (pdf)

Fall 2020: Randomness and Computation

Course No:
EECS 598-013
Instructor:
Mahdi Cheraghchi
Prerequisites:
Grad standing or permission of instructor

Along with time and memory, randomness is a fundamental resource in computation. In many cases, randomness can be used to speed up computational tasks or reduce the memory footprint, creating an entire area of randomized algorithms. In applications such as cryptography, randomness is a necessary aspect of computation and such system crucially rely on access to high quality random bits (for example to produce a perfectly random secret key). Moreover for such applications, the information-theoretic view of randomness is used to mathematically model uncertainty. In machine learning and computational learning, randomness and statistics are essential tools to model the computational task. The use of randomness and particularly the probabilistic method constitutes an important proof technique in discrete mathematics. The range of applications of randomness in computation is simply too broad to break down on a short list.

The aim of this course is to provide a mathematically rigorous exposition of the role of randomness in computation. The course will do so by showcasing examples from different application areas, such as those described above. Furthermore, time permitting, the question of simulating randomness by deterministic computation as well as extraction of randomness from weak random sources will be discussed. The precise choice of topics within the area will be flexible depending on the interests of the audience and active feedback from the students.

More info (pdf)

Fall 2020: Program Synthesis Techniques and Applications

Course No:
EECS 598-006
Instructor:
Xinyu Wang
Machine programming aims to achieve this goal by automatically turning the “problem definition” into “instructions” that can be executed on machines. It has the potential to revolutionize the way software is developed. In fact, this revolution has already begun. For example, deep learning has reshaped our lives in many aspects, where the “problem definition” is expressed using data and “instructions” are machine learned models.
This special topics course will cover another important and emerging class of machine programming techniques, namely program synthesis, which is an area that sits at the intersection of programming languages, formal methods, artificial intelligence, programming systems, and has a wide spectrum of applications, e.g., in end-user programming, data science, databases, systems, software engineering, architecture, robotics, human-computer interaction, etc. In this course, we will study state-of-the-art program synthesis techniques as well as their applications and implementations.
More info (pdf)

Fall 2020: Formal Verification of Hardware and Software Systems

Course No:
EECS 598-008
Instructor:
Karem Sakallah
Prerequisites:
Graduate standing in CSE

The course will be based on reading classic papers on software and hardware verification as well as more recent papers that describe advances in automated reasoning algorithms and their applications to verification. A theme of the course will be to find common threads that tie together the seemingly‐disparate methods used in HW and SW verification. Students can expect to learn how to encode software programs and hardware circuits as transition systems, and how to develop suitable abstractions for checking control‐centric properties. Additionally, they will have hands‐on experience with a variety of existing reasoning and verification tools and the option of developing extensions to these tools. The course work will consist of a few short assignments to help the students master the main technical issues and semester‐long individual or group projects selected by the students.

More info (pdf)

Fall 2020: Human-Computer Interaction

Course No:
EECS 598-002
Instructor:
Nikola Banovic
Prerequisites:
Graduate standing

This course will teach students principles and methods of technical Human-Computer Interaction (HCI) research. It will also include a survey of important research threads. Short individual assignments will give students exposure to existing research methods in HCI. Midterm and final exams will test the student knowledge of the topic.

More info (pdf)

Fall 2020: Approximation Algorithms and Hardness of Approximation

Course No:
EECS 598-010
Instructor:
Euiwoong Lee
Prerequisites:
EECS 376 and 477

Approximation algorithms have been actively studied in both algorithms and complexity theory, culminating in optimal approximation algorithms for some fundamental problems; they achieve some approximation guarantees and no polynomial time algorithm can do better under some complexity conjectures. The theory of approximation algorithms also leads to beautiful connections between algorithms, complexity, and some areas of mathematics. This course will provide an overview of these connections, stressing techniques and tools required to prove both algorithms and complexity results.

More info (pdf)

Fall 2020: Introduction to Algorithmic Robotics

Course No:
EECS 498-001
Instructor:
Dmitry Berenson
Prerequisites:
EECS 280 is required; EECS 281 and MATH 214 are recommended

 Build the foundation for your future in robotics!

  • Convex Optimization
  • Motion Planning
  • Point Cloud Processing
  • Probabilistic Reasoning
  • Kalmanand Particle Filters
More info (pdf)

Fall 2020: Semiconductor Power Devices

Course No:
EECS 598-001
Instructor:
Becky Peterson
Prerequisites:
EECS 320 or 421 or grad standing

Power devices drive our society, from the power grid and renewable energy integration to hybrid and electric vehicles, trains, space exploration, and industrial and consumer electronics. This course will cover design and operating principles of semiconductor devices for discrete and integrated power electronics. Devices we will discuss include the power MOSFET, IGBT, HEMT, Schottky and PIN diodes, as well as emerging device architectures. We will study wide bandgap semiconductor materials, device fabrication and packaging required for power devices, including GaN, SiC, and Ga2O3. Students will be exposed to numerical device modeling using commercial TCAD software (Synopsys Sentaurus and Silvaco Atlas), and will do a final group presentation on a topic of their choice.

More info (pdf)

Fall 2020: Cybersecurity for Future Leaders

Course No:
EECS 498-004
Instructor:
Mustafa Jave Ali
Prerequisites:
None

This class will examine the broad landscape of cybersecurity from both a technical and policy perspective. It will introduce fundamental concepts of computing and cyber security, including information theory, computability, cryptography, networking fundamentals, how vulnerabilities arise, and how attacks work. In addition, it will explore foundational ideas including definitions, cyber norms, and ethics; identify existing U.S. laws, authorities and governmental constructs; and frame classic security concepts like deterrence, attribution, offense, defense, and retaliation. The course will also involve guest speakers, student panels, and writing assignments designed to capture technical and policy insights, and a simulated meeting where students assume different governmental or private sector roles to examine potential courses of action regarding a cybersecurity crisis scenario.

More info (pdf)

Fall 2020: Election Cybersecurity

Course No:
EECS 498-005
Instructor:
J. Alex Halderman
Prerequisites:
EECS 388 or permission of instructor

Elections, the foundation of democracy,are increasingly subject to electronic attacks. Manipulation of social media, hacks against campaigns, and vulnerabilities in voting equipment create unprecedented risks.

This special topics course will examine the past, present, and future of election security, informed by perspectives at the intersection of computer science, law and public policy, politics, and international affairs.

We will study how elections can be attacked and work to help defend them, using a broad range of technical and public policy tools.
More info (pdf)

Fall 2020: Reinforcement Learning Theory

Course No:
EECS 598-003
Instructor:
Lee Ying
Prerequisites:
EECS 501 or equivalent or grad standing

This course covers fundamental theories and principles of reinforcement learning.

More info (pdf)

Winter 2020: Convex Optimization Methods in Control

Course No:
EECS 598-017
Credit Hours:
3 credits
Instructor:
Peter Seiler
Prerequisites:
EECS 560 (AERO 550)(MECHENG 564) Linear Systems Theory or permission of instructor

Convex optimization plays a central role in the numerical solution of many design and analysis problems in control theory. This course focuses on the practical aspects of using convex optimization methods to solve these problems. See flyer for more information.

More info (pdf)

Winter 2020: Quantum Information, Probability and Computing

Course No:
EECS 598-005
Credit Hours:
3 credits
Instructor:
S. Sandeep Pradhan

See course flyer for more information.

More info (pdf)

Winter 2020: Reinforcement Learning

Course No:
EECS 598-002
Credit Hours:
3 credits
Instructor:
Lei Ying
Prerequisites:
EECS 502 or equivalent

This course covers fundamental theories and principles of reinforcement learning. Topics to be covered include:

  1. Dynamic programming and the principle of optimality
  2. Multi-armed bandit: epsilon-greedy, Upper Confidence Bound (UCB) algorithm, Thompson Sampling
  3. Markov chains and Markov Decision Process (MDP)
  4. Value iteration, policy iteration, and LP formulation
  5. Q-Learning: Model-based and model-free
  6. Linear function approximation and deep reinforcement learning
  7. Temporal-difference learning
  8. SARSA
  9. Policy gradient algorithm and variance reduction
  10. The ODE methods and convergence analysis
More info (pdf)

Winter 2020: Human-Computer Interaction

Course No:
EECS 598-012
Credit Hours:
3 credits
Instructor:
Nikola Banovic
Prerequisites:
Grad standing or permission of instructor

This course will teach students principles and methods of technical Human-Computer Interaction (HCI) research. It will also include a survey of important research threads. Short individual assignments will give students exposure to existing research methods in HCI. Midterm and final exams will test the student knowledge of the topic.

More info (pdf)

Winter 2020: Applied Machine Learning for Affective Computing

Course No:
EECS 498-005 / EECS 598-010
Credit Hours:
3 credits
Instructor:
Emily Mower Provost
Prerequisites:
EECS 281 and (MATH 214 or MATH 217 or MATH 296 or MATH 417) or graduate standing

This course covers the concepts and techniques that underlie machine learning of human behavior across multiple interaction modalities. Topics include: speech/text/gestural behavior recognition through applications of machine learning, including deep learning. Fluency in a standard object-oriented programming language is assumed. Prior experience with speech or other data modeling is neither required nor assumed.

More info (pdf)

Winter 2020: Advanced Energy Storage

Course No:
EECS 598-014
Credit Hours:
3 credits
Instructor:
Ziyou Song
Prerequisites:
EECS 560 or equivalent

 This course primarily focuses on introducing and comparing different energy storages, such as pumped-storage, compressed air energy storage, batteries, capacitive energy storage, fuel cells, and flywheels, with special applications to electrified vehicles and renewable energy systems where energy storage plays a crucial role. 

The course will focus on reviewing principles and recent progress in energy storage systems, with the goals of improving the performance and lifespan of electrified vehicles as well as integrating renewable energy (e.g., wind and solar energy) into the grid. 

More info (pdf)

Winter 2020: Engineering Interactive Systems

Course No:
EECS 598-015
Credit Hours:
3 credits
Instructor:
Alanson Sample
Prerequisites:
General programming skills

Classroom instruction will focus on a review of current research topics and literature in technical HCI areas, including interactive technologies, augmented reality, haptics, wearables, shape-changing interfaces, and more. Homework assignments will take the form of mini-projects designed to build hands-on skills in the use of laser cutters, 3D printers, sensing and signal acquisition, embedded systems, and machine learning for event and activity recognition. The class will culminate in a final project where teams of students will pitch, build, and demo a self-defined project using the skills developed in this course.

More info (pdf)

Winter 2020: Quantum Computers: Fundamentals, Architectures, and Programming

Course No:
EECS 498-006 / EECS 598-013
Credit Hours:
3-4 credits
Instructor:
Pinaki Mazumder
Prerequisites:
Basic knowledge of linear algebra

Quantum information has long outgrown the limits of academic exploration of a new kind of secure cryptography realized by quirky features of quantum systems. Theoretical investigations revealed that quantum computers while defying the common approach to programming may greatly outperform classical architectures. The emergent new generation of information processing has given birth to the emerging multi-billion-dollar industry by utilizing different approaches to processing quantum information. Quantum architectures designed by D-wave, IBM, Google, Rigetti Computing, Intel, and Ion-Q exploit a wide gamut of innovative technologies to implement disparate paradigms of quantum computation. On the application side, Google, NASA, Microsoft and other companies heavily invest into development of quantum artificial intelligence, machine learning, and complex optimization problems.

The present course aims to meet the industrial interest in engineers with a specialized training capable of creating and developing new applications utilizing quantum information processing architectures. An indispensable part of the course is a series of programming assignments that will be designed to impart practical experience with quantum computers: starting from basic operations with qubits utilizing individual quantum gates to applications with complex functionality. Students will use commercial graded simulators such as Qiskit, QX, and PyQu to implement their programming assignments. All technical formalism needed for the topics covered in the course will be introduced in the course.

More info (pdf)

Winter 2020: The Ecological Approach to Visual Perception

Course No:
EECS 598-007
Credit Hours:
3 credits
Instructor:
David Fouhey
Prerequisites:
Graduate standing in EECS or Robotics or permission of instructor

Specifically, we will explore (in no particular order): the perception of affordances and spatial layout; perception of and for manipulation; agents and how they exist in their environment; visual navigation; learning from demonstration and natural supervision; learning of physical models and dynamics; and learning of agency and intentionality. While the primary focus and assumed background knowledge is learning-based visual perception, readings will come from a wide variety of fields and students should be prepared to read out of their comfort zone.

This is a graduate-level course incorporating two components. The first is weekly group-driven reading and active discussion and debating of related work in robotics, computer vision, machine learning, and psychology. This will be a roughly even split between recent work and classics. The second are projects that put ideas from the first component to the test. These are semester-long projects, ideally interdisciplinary, that: find a particular problem; make a concrete hypothesis and experiments to test it; and execute them computationally using realistic data.

More info (pdf)

Winter 2020: Software Defined Radio

Course No:
EECS 398-001
Credit Hours:
4 credits
Instructor:
Wayne Stark
Prerequisites:
EECS 216 or permission of instructor

In this class you will learn basic concepts of software defined radio.  You will learn the following

  1. How basic radios work
    • Upconversion and down conversion
    • Frequency and phase synchronization
    • Timing synchronization
    • Digital modulation and demodulation including BPSK, QPSK, QAM, FSK, OFDM  (several of these are used in 5G cellular networks and WiFi).
    • How to implement in software the different modulation and demodulation schemes.  Most of what we do is done via a graphical user interface (GUI) but some custom operation can be programmed using Python.  No knowledge of Python is assumed.
  2. How to implement these in software and hardware.  We will use Universal Software Radio Peripheral (USRP) for the hardware and GNU Radio Companion (GRC) for the software. This hardware and software is used by many companies including Samsung, Nokia, AT&T, Navy, Sandia National Labs, MIT, Analog Devices, Oak Ridge National Laboratory, Xilinx, IBM, Northrop Grumman, NASA, IEEE.
More infoMore info (pdf)

Winter 2020: Quantum Optoelectronics

Course No:
EECS 598-004
Credit Hours:
3 credits
Instructor:
Mackillo Kira
Prerequisites:
PHYSICS 240 AND (EECS 334 or EECS 434 or EECS 320 or EECS 520 or EECS 540)

Optoelectronic devices are already being revolutionized by the prospects of quantum technology. Ever smaller and faster components will inevitably reach a level where a collective can outperform individual parts due to emergent quantum effects such as entanglement. This lecture welcomes you to the central concepts of quantum engineering of semiconductors to explore optoelectronic, quantum-optical, and many-body processes, relevant for state-of-the-art experiments and the future of quantum technology.

More info (pdf)

Fall 2019: Building Computer-Based Supports for Student Learning Through Inquiry

Course No:
EECS 498-009
Credit Hours:
4 credits
Instructor:
Elliot Soloway and Mark Guzdial
Prerequisites:
Senior standing

In this 498, the goal will be to build software to support educational activities in K-12 and in higher education. Faculty in K-12 have suggested the need for specific pieces of software. For example, a 3rd grade teacher has been pleading for a T-Chart app one that is collabrified, i.e., it supports synchronous collaboration. In higher ed, in materials courses, there is a need for a VR app to help students visualize the atomic structure of the materials. In high school, there is a need for a tool to support historical inquiry. In past iterations of this project, students have built software that is actually used in schools, nationwide. Teams will be formed; they will use the agile software development methodology: cycles of design, build, user test. Be prepared to visit classrooms and hear first-hand what users think of your software!

More info (pdf)

Fall 2019: Computational Modeling in HCI

Course No:
EECS 598-002
Credit Hours:
3 credits
Instructor:
Nikola Banovic
Prerequisites:
Programming experience in Java, Python, MATLAB or R

This seminar style course will teach students methods to track, collect, and express human behavior data as computational models of behavior. The course will have a particular focus on computational approaches to describe, simulate, and predict human behavior from empirical behavior traces data. It will contrast computational modeling with other methodologies to understand human behavior and compare computational modeling with existing behavior modeling methodologies in Human-Computer Interaction (HCI). Short individual assignments will give students exposure to existing modeling methods in HCI. Large,group-based final project will give students an opportunity to push the boundaries of computational modeling in HCI by modeling behaviors of their choice from an existing data set to design and implement a novel Computational Modeling system from scratch.

More info (pdf)

Fall 2019: Power System Markets and Optimization

Course No:
EECS 598-004
Credit Hours:
3 credits
Instructor:
Johanna Mathieu
Prerequisites:
EECS 463 or permission of instructor

This course covers the fundamentals of electric power system markets and the optimization methods required to solve planning and operational problems including economic dispatch, optimal power flow, and unit commitment. The course will highlight recent advances including convex relaxations of the optimal power flow problem, and formulations/solutions to stochastic dispatch problems. Problems will be placed in the context of actual electricity markets, and new issues, such as incorporation of renewable resources and demand response into markets, will be covered. All students will conduct an individual research project.

More info (pdf)

Fall 2019: Applied Parallel Programming with GPUs

Course No:
EECS 498-003
Credit Hours:
4 credits
Instructor:
Reetuparna Das
Prerequisites:
EECS 281 and EECS 370

The goal of this class is to teach parallel computing anddeveloping applications for massively parallel processors (e.g.GPUs). Self driving cars, machine learning and augmentedreality are examples of applications involving parallel computing. The class focuses on computational thinking, forms of parallelism, programming models, mapping computations to parallel hardware, efficient data structures, paradigms for efficient parallel algorithms, and application case studies.

The course will cover popular programming interface for graphics processors (CUDA for NVIDIA processors), internal architecture of graphics processors and how it impacts performance, and implementations of parallel algorithms on graphics processors. The curriculum will be delivered in ~29 lectures. The class has heavy programming components, including six hands-on assignmentsand a final project.

More info (pdf)

Fall 2019: Laser Plasma Diagnostics

Course No:
EECS 598-004
Credit Hours:
3 credits
Instructor:
Louise Willingale
Prerequisites:
EECS 537 or permission of instructor

High power laser pulses are used to both create and diagnose high-energy density systems. In this course, we will discuss the techniques used for creating, characterizing and timing high power laser pulses from megajoule-nanosecond pulses to relativistic-intensity femtosecond pulses. We will explore the diagnostics used to characterize high-energy density plasmas through opticaland other radiation measurements as well as backlighting techniques. Other important aspects of performing experiments, such as target positioning techniques, will be touched on. In addition to the material discussed in lectures, students will consider real experimental data and recent research publications to learn analysis techniques, gain appreciation for physical limitations (such as instrument resolution and background signals), and comparison with theoretical models. This course is suitable for graduate students studying plasma physics, optics and laser science and other related areas. A design project based aroundan experimental proposal will involve a peer review process, written proposal and oral presentation

More info (pdf)

Fall 2019: VLSI for Communication and Machine Learning

Course No:
EECS 598-006
Credit Hours:
3-4 credits
Instructor:
Hunseok Kim
Prerequisites:
EECS 351 and (EECS 312 or EECS 370) or grad standing

This course will survey methodologies to design energy efficient and/or high-performance VLSI systems for the state-of-the-art wireless communication, machine learning, and signal processing systems. The primary focus of the course is on designing hardware efficient algorithms and energy-aware VLSI IC architectures to deliver the performance and efficiency required by various signal processing and machine learning applications. The course will be a mix of lectures and student-led presentations/projects. The content will be suitable for senior undergraduates or graduate students interested in hardware-efficient algorithms and their VLSI implementations.

More info (pdf)

Fall 2019: Topics in Surveillance: Law and Technology

Course No:
EECS 598-007
Credit Hours:
1 credit
Instructor:
J. Alex Halderman and Margo Schlanger
Prerequisites:
Grad standing or permission of instructor

This unique seminar brings together students and faculty from computer science and law to address six current controversies in surveillance, chosen from topics like:-smartphone hacking by the FBI-internet and telephone metadata collection-border searches of electronic devices-mass surveillance of data and phone calls-cellphone geolocation tracking.

More info (pdf)

Fall 2019: Brain-Inspired Computing: Models, Architectures, and Programming

Course No:
EECS 598-001
Credit Hours:
3-4 credits
Instructor:
Pinaki Mazumder
Prerequisites:
Permission of instructor

Brain-inspired computing is a subset of AI-based machine learning and is generally referred to both deep and shallow artificial neural networks (ANN) and spiking neural networks (SNN). Deep convolutional neural networks (CNN) have made pervasive market inroads in numerous commercial applications and their software implementations are widely studied in computer vision, speech processing and other courses. The purpose of this course will be to study the wide gamut of shallow and deep neural network models, the methodologies for specialized hardware design of popular learning algorithms, as well as adapting hardware architectures on crossbar fabrics of emerging technologies such as memristors and spin torque nonmagnetic devices. Existing software development tools such as TensorFlow, Caffe, and PyTorch will be leveraged to teach various aspects of neuromorphic designs.

More info (pdf)

Fall 2019: Crowdsourcing and Human-AI Interaction

Course No:
EECS 598-008
Credit Hours:
4 credits
Instructor:
Walter Lasecki
Prerequisites:
Graduate standing in CSE or SI

This course will cover topics in Human-Computer Interaction, human computation and crowdsourcing, and the emerging literature in Human-AI Interaction, with a focus on techniques for creating interactive intelligent systems that leverage a combination of human and machine intelligence to accomplish tasks more effectively than either could alone. We will also touch on the theory underlying many of the current approaches (e.g., game theory, voting theory, reasoning under uncertainty, and machine learning), and potential ethical concerns raised by these systems (e.g., safety and end-user privacy).

More info (pdf)
Show All