INTRO-ELECTRICAL ENGINEERING
Basic concepts of electrical engineering. Exploration of selected topics and their application. Electrical variables, circuit laws, nonlinear and linear elements, ideal and real sources, transducers, operational amplifiers in simple circuits, external behavior of diodes and transistors, first order RC and RL circuits. Digital representation of a signal, digital logic gates, flipflops. A lab is an integral part of the course. Required of electrical engineering and computer engineering majors.
- MW 4:10 p.m.-5:25 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 3.5 points
DIGITAL SYSTEMS LABORATORY
Companion lab course for CSEE W3827. Experiments cover such topics as logic gates; flip-flops; shift registers; counters; combinational logic circuits; sequential logic circuits; programmable logic devices. The lab generally meets on alternate weeks.
- M 4:10 p.m.-6:40 p.m.
- T 4:10 p.m.-6:40 p.m.
- F 1:10 p.m.-3:40 p.m.
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Instructor:
Kenneth Shepard
CULPA: 2 Wikipedia h-index: 64 Info - 1 points
ELECTRONIC CIRCUITS LABORATORY
Companion lab course for ELEN E3331. Experiments cover such topics as macromodeling of nonidealities of opamps using SPICE; Schmitt triggers and astable multivibrations using op-amps and diodes; logic inverters and amplifiers using bipolar junction transistors; logic inverters and ring oscillators using MOSFETs; filter design using opamps. The lab generally meets on alternate weeks.
- M 4:10 p.m.-6:40 p.m.
- T 4:10 p.m.-6:40 p.m.
- F 1:10 p.m.-3:40 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 1 points
ELECTRONIC CIRCUITS
Operational amplifier circuits. Diodes and diode circuits. MOS and bipolar junction transistors. Biasing techniques. Small-signal models. Single-stage transistor amplifiers. Analysis and design of CMOS logic gates. A/D and D/A converters.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 3 points
EE SENIOR DESIGN PROJECT
Students work in teams to specify, design, implement and test an engineering prototype. Involves technical as well as non-technical considerations, such as manufacturability, impact on the environment, economics, adherence to engineering standards, and other real-world constraints. Projects are presented publicly by each design team in a school-wide expo.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
David G Vallancourt
CULPA: 28
Info
- 3 points
ELECTROMAGNETICS
Basic field concepts. Interaction of time-varying electromagnetic fields. Field calculation of lumped circuit parameters. Transition from electrostatic to quasistatic and electromagnetic regimes. Transmission lines. Energy transfer, dissipation, and storage. Waveguides. Radiation.
- MW 2:40 p.m.-3:55 p.m.
- F noon-2 p.m.
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Instructor:
Keren Bergman
CULPA: 1 Wikipedia h-index: 69 Info - 4 points
INTRO TO COMMUNICATION SYSTEMS
A basic course in communication theory, stressing modern digital communication systems. Nyquist sampling, PAM and PCM/DPCM systems, time division multipliexing, high frequency digital (ASK, OOK, FSK, PSK) systems, and AM and FM systems. An introduction to noise processes, detecting signals in the presence of noise, Shannons theorem on channel capacity, and elements of coding theory.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Irving Kalet
CULPA: 3
h-index: 12
Info
- 3 points
Computing with Brain Circuits of Model O
Building the functional map of the fruit fly brain. Molecular transduction and spatio-temporal encoding in the early visual system. Predictive coding in the Drosophila retina. Canonical circuits in motion detection. Canonical navigation circuits in the central complex. Molecular transduction and combinatorial encoding in the early olfactory system. Predictive coding in the antennal lobe. The functional role of the mushroom body and the lateral horn. Canonical circuits for associative learning and innate memory. Projects in Python.
- M 7 p.m.-9:30 p.m.
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Instructor:
Aurel A Lazar
h-index: 53 Info - 3 points
MOD DISPLAY SCI & TECHNOLOGY
Introduction to modern display systems in an engineering context. The basis for visual perception, image representation, color space, metrics of illumination. Physics of luminescence, propagation and manipulation of light in anisotropic media, emissive displays, and spatial light modulators. Fundamentals of display addressing, the Alt-Pleshko theorem, multiple line addressing. Large area electronics, fabrication, and device integration of commercially important display types. A series of short laboratories will reinforce material from the lectures. Enrollment may be limited.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Ioannis Kymissis
CULPA: 4 h-index: 48 Info - 3 points
COMPUTER HARDWARE DESIGN
Practical aspects of computer hardware design through the implementation, simulation, and prototyping of a PDP-8 processor. High-level and assembly languages, I/O, interrupts, datapath and control design, pipelining, busses, memory architecture. Programmable logic and hardware prototyping with FPGAs. Fundamentals of VHDL for register-transfer level design. Testing and validation of hardware. Hands-on use of industry CAD tools for simulation and synthesis.
- MWF 8:40 a.m.-9:55 a.m.
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Instructor:
Tanvir Ahmed Khan
Info - 3 points
DISCRETE CONTROL SYSTEMS
The course studies control strategies and their implementation in the discrete domain. Introduction with examples; review of continuous control and Laplace Transforms; review of continuous state-space representation and Solutions; review of difference equations, discretization in time and frequency, the WKS (aka Shannon) sampling theorem, windowing, filters, Transforms: Fourier series, Fourier transform, z-transform and their inverses; Ideal sampler, Sample-and-hold devices, zero, one, polygonal, and slewer hold; Transfer functions, block diagrams, and signal flow graphs for discrete systems; Discrete State-Space transformation, controllabililty, observability, and stability in the state-space domain. Discrete time and z domain analysis, steady state analysis, discrete-time root-locus, and pole-zero placement; Discrete Nyquist stability criterion, Bode plot, Gain and Phase Margin analysis, Nichols chart, bandwidth and sensitivity analysis; Design criteria, self-tuning regulator, Kalman filter, and simulation, followed by advanced stability analysis such as Lyapunov stability; Overview of the discrete Euler-Lagrange equations, discrete maximum and minimum principle, optimal linear discrete regulator design, optimality and dynamic programming.
- W 4:10 p.m.-6:40 p.m.
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Instructor:
Homayoon Beigi
Info - 3 points
DIGITAL COMMUNICATIONS
Digital communications for both point-to-point and switched applications is further developed. Optimum receiver structures and transmitter signal shaping for both binary and M-ary signal transmission. An introduction to block codes and convolutional codes, with application to space communications.
- W 7 p.m.-9:30 p.m.
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Instructor:
Alexei Ashikhmin
Info - 3 points
Quantum Optimization and Machine Learnin
QUANTUM OPT & ML
An introduction to the recent development in quantum optimization and quantum machine learning using gate-based Noisy Intermediate Scale Quantum (NISQ) computers. IBM’s quantum programming framework Qiskit is utilized. Qbits, quantum gates and quantum measurements, quantum algorithms (Grover’s search, Simon’s algorithm, quantum Fourier transform, quantum phase estimation) quantum optimization (quantum annealing, QAOA, variational quantum eigensolver), quantum machine learning (quantum support vector machine, quantum neural networks).
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Xiaodong Wang
CULPA: 15 Info - 3 points
RANDOM SIGNALS & NOISE
Characterization of stochastic processes as models of signals and noise; stationarity, ergodicity, correlation functions, and power spectra. Gaussian processes as models of noise in linear and nonlinear systems; linear and nonlinear transformations of random processes; orthogonal series representations. Applications to circuits and devices, to communication, control, filtering, and prediction.
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Irving Kalet
CULPA: 3
h-index: 12
Info
- 3 points
Image Processing and Computer Vision
Image Proc and Comp Visio
Introduction to the mathematical tools and algorithmic implementation for representation, processing, and reconstruction of digital images, videos, and visual sensory data. Image representation, filtering, transform, quality enhancement, restoration, feature extraction, object segmentation, motion analysis, stitching, depth estimation, 3D reconstruction, classification, and coding for data compression. A series of programming assignments reinforces material from the lectures.
- R 10:10 a.m.-12:40 p.m.
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Instructor:
Christine P Hendon
Wikipedia h-index: 21 Info - 3 points
TOPICS IN EE & CE
Stat Learning Quant Tradi
Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 4900 to 4909.
TPC NEUROSCI & DEEP LEARN
Comp. w. Brain Cir of MO
Selected advanced topics in neuroscience and deep learning. Content varies from year to year, and different topics rotate through the course numbers 6070 to 6079.
- M 7 p.m.-9:30 p.m.
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Instructor:
Aurel A Lazar
h-index: 53 Info - 3 points
ADV DIGITAL ELECTRONIC CIRCUIT
Advanced topics in the design of digital integrated circuits. Clocked and non-clocked combinational logic styles. Timing circuits: latches and flip-flops, phase-locked loops, delay-locked loops. SRAM and DRAM memory circuits. Modeling and analysis of on-chip interconnect. Power distribution and power-supply noise. Clocking, timing, and synchronization issues. Circuits for chip-to-chip electrical communication. Advanced technology issues that affect circuit design. The class may include a team circuit design project.
- TR 2:40 p.m.-3:55 p.m.
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Instructor:
Mingoo Seok
h-index: 31 Info - 3 points
SEMICONDUCTOR DEVICE PHYSICS
Physics and properties of semiconductors. Transport and recombination of excess carriers. Schottky, P-N, MOS, and heterojunction diodes. Field effect and bipolar junction transistors. Dielectric and optical properties. Optical devices including semiconductor lamps, lasers, and detectors.
- R 4:10 p.m.-6:40 p.m.
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Instructor:
Xiang Meng
h-index: 8 Info - 3 points
Future Energy: Economics, Systems, Polic
Holistic understanding of the science and socioeconomic modelling of climate change, the properties of renewable energy sources, their economic and engineering
implications, and the policy decisions in the transition to future energy. Fundamentals of systems needed to support deep penetration of renewable energy, including grid stability, transmission and storage systems, and the design and operations of carbonaware datacenters.
- MW 11:40 a.m.-12:55 p.m.
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Instructor:
Debasis Mitra
Wikipedia h-index: 61 Info - 3 points
Mathematics of Machine Learning, Signals
Math of ML, Signals, & Co
Advanced Linear Algebra, Complex Variable Theory, Integral Transforms, Measure Theory and Probability Theory, Advanced Information Theory, Differential and Difference Equations, Calculus of Variations, Nonlinear Optimization, State-Space Modeling, Advanced Signal Processing and Recognition: non-Stationary Signal Recognition, Spectral and Cepstral Analysis, Supervised and Unsupervised Clustering, Decision Theory, Math of modern NN architectures.
- M 4:10 p.m.-6:40 p.m.
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Instructor:
Homayoon Beigi
Info - 3 points
MODERN CONTROL THEORY
- TR 10:10 a.m.-11:25 a.m.
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Instructor:
James Anderson
h-index: 18 Info - 3 points
TOPICS DATA-DRIVEN ANAL & COMP
TPC: MATHEMATICS OF DEEP LEARN
Selected advanced topics in data-driven analysis and computation. Content varies from year to year, and different topics rotate through the course numbers 6690 to 6699.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
Predrag R Jelenkovic
CULPA: 1 h-index: 35 Info - 3 points
BAYESIAN MOD MACHINE LEARNING
Basic statistics and machine learning strongly recommended. Bayesian approaches to machine learning. Topics include mixed-membership models, latent factor models, Bayesian nonparametric methods, probit classification, hidden Markov models, Gaussian mixture models, model learning with mean-field variational inference, scalable inference for Big Data. Applications include image processing, topic modeling, collaborative filtering and recommendation systems.
- M 1:10 p.m.-3:40 p.m.
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Instructor:
John W Paisley
CULPA: 1 Info - 3 points
Advanced Deep Learning
A graduate level deep learning course focusing on attention and transformer architectures. Introduces selection of transformer-based models applied to language, vision and audio problems, generative, diffusion, state-space, and mixture of expert models. Analysis of zero-shot learning, hallucinations, graph neural networks, multimodal architectures, and agents. Multiple object detection methods are compared. Course is executed in hybrid presentation/flipped-classroom styles, with practical exercises and student presentations on design and operation of DL models.
- W 10:10 a.m.-12:40 p.m.
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Instructor:
Zoran Kostic
CULPA: 1 h-index: 24 Info - 3 points
Deep Learning on the Edge
Advanced-level lab-based course. Build and experiment with deep-learning models implemented on low-power GPU edge computing device. Topics: architectures of low-power GPU devices, algorithms and DL models suitable for edge implementation, CUDA language, pre-processing of data, labeling for ground-truth annotation, profiling techniques, connectivity between edge devices and cloud computing servers, real-life data for experimentation, comparison of performance for a variety of methods, comparison of performance of edge-computing and cloud computing approaches.
- W 1:10 p.m.-3:40 p.m.
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Instructor:
Zoran Kostic
CULPA: 1 h-index: 24 Info - 3 points
SPEECH&AUDIO PROC&REC
Fundamentals of digital speech processing and audio signals. Acoustic and perceptual basics of audio. Short-time Fourier analysis. Analysis and filterbank models. Speech and audio coding, compression, and reconstruction. Acoustic feature extraction and classification. Recognition techniques for speech and other sounds, including hidden Markov models.
- T 4:10 p.m.-6:40 p.m.
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Instructor:
Homayoon Beigi
Info - 3 points
Statistical signal processing and learni
STATISTICAL SIGNAL PROCES
Introduction to the fundamental principles of statistical signal processing related to detection and estimation. Hypothesis testing, signal detection, parameter estimation, signal estimation, and selected advanced topics. Suitable for students doing research in communications, control, signal processing, and related areas.
- M 10:10 a.m.-12:40 p.m.
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Instructor:
Guido H Jajamovich
Info - 3 points
Sparse and Low-Dimensional Models for Hi
SPARSE MODELS FOR HI-D DA
Overview of theory, computation and applications for sparse and low-dimensional data modeling. Recoverability of sparse and low-rank models. Optimization methods for low-dim data modeling. Applications to imaging, neuroscience, communications, web data.
- R 1:10 p.m.-3:40 p.m.
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Instructor:
John Wright
CULPA: 1 h-index: 44 Info - 3 points
TOPICS IN SIGNAL PROCESSING
INTRO TO BLOCKCHAIN TECH
Advanced topics in signal processing, such as multidimensional signal processing, image feature extraction, image/video editing and indexing, advanced digital filter design, multirate signal processing, adaptive signal processing, and wave-form coding of signals. Content varies from year to year, and different topics rotate through the course numbers 6880 to 6889.
- F 8:10 a.m.-10 a.m.
- 3 points
TOPICS-INFORMATION PROCESSING
Quantum Engineering
This course covers the fundamentals of quantum engineering, including core quantum information concepts, noise and decoherence in quantum hardware, as well as basic quantum control and error correction strategies.
- R 4:10 p.m.-6:40 p.m.
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Instructor:
Zhaoyou Wang
Info - 3 points
TOPICS-INFORMATION PROCESSING
Reinforcement Learning
Advanced topics spanning electrical engineering and computer science such as speech processing and recognition, image and multimedia content analysis, and other areas drawing on signal processing, information theory, machine learning, pattern recognition, and related topics. Content varies from year to year, and different topics rotate through the course numbers 6890 to 6899.
- R 10:10 a.m.-12:40 p.m.
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Instructor:
Javad Ghaderi
h-index: 17 Info - 3 points
TOPICS IN ELECTRICAL AND COMPUTER ENGINE
Embedded AI
Selected topics in electrical and computer engineering. Content varies from year to year, and different topics rotate through the course numbers 6900 to 6909.
- M 1:10 p.m.-3:40 p.m.
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Instructor:
Xiaofan Jiang
h-index: 24 Info - 3 points