Topic 1: Detection Theory. Electrical and Computer Engineering 381K (Topic 1) and Electrical Engineering 381K (Topic 1) may not both be counted.

Topic 2: Digital Communications. Explore characterization of baseband and passband communication signals and channels, digital modulation, and pulse shaping; optimum receivers in additive white Gaussian noise including matched-filtering, ML, and MAP detection and their bit error probability for M-ary modulation; inter-symbol interference channels, FIR and IIR equalization, multicarrier modulation, OFDM and frequency domain equalization. Introduction to information theory, channel capacity, error control codes, and ML sequence detection (Viterbi decoding). Examine applications to stationary wireless and wireline channels. Electrical and Computer Engineering 381K (Topic 2) and Electrical Engineering 381K (Topic 2) may not both be counted. Additional prerequisite: Knowledge in probability and random processes, digital signal processing, and digital communications.

Topic 3: Applied Machine Learning. Examine approaches to extracting valid and useful insights from data using statistically oriented approaches, with emphasis on predictive models learned from large-scale data sets. Code machine learning algorithms in Python and use various data visualization techniques. Only one of the following may counted: Electrical and Computer Engineering 380L (Topic 10), 381K (Topic 3), Electrical Engineering 380L (Topic 10).

Topic 4: Statistical Machine Learning. Introduction to the theoretical underpinnings of machine learning and the derivations that transform those foundations into practical algorithms. Discuss empirical risk minimization and the probably approximately correct (PAC) learning framework; the bias-complexity tradeoff; the Vapnik-Chervonenkis dimension; Rademacher complexities; linear predictors; boosting; model selection and validation; convex learning problems; support vector machines and kernel methods; decision trees; clustering; dimensionality reduction; generative models; and variational Bayesian methods. Only one of the following may be counted: Electrical and Computer Engineering 381K (Topic 4), Electrical and Computer Engineering 381V (Topic: Statistical Machine Learning) and Electrical Engineering 381V (Topic: Statistical Machine Learning).

Topic 5: Advanced Telecommunication Networks. Explore methods and research issues in the performance evaluation and management of high-speed and mobile communication networks. Electrical and Computer Engineering 381K (Topic 5) and Electrical Engineering 381K (Topic 5) may not both be counted. Additional prerequisite: Electrical and Computer Engineering 380N (Topic 11) (or Electrical Engineering 380N (Topic 11)), 381J (or Electrical Engineering 381J), and 381K (Topic 13) (or Electrical Engineering 381K (Topic 13)).

Topic 6: Estimation Theory. Electrical and Computer Engineering 381K (Topic 6) and Electrical Engineering 381K (Topic 6) may not both be counted.

Topic 7: Information Theory. Explore source and channel coding theorems, Kolmogorov complexity, network information theory, and connections with large deviations. Electrical and Computer Engineering 381K (Topic 7) and Electrical Engineering 381K (Topic 7) may not both be counted. Additional prerequisite: Knowledge in communications systems.

Topic 8: Digital Signal Processing. Examine signals and systems; generalized functions; z-transforms; Fourier series and transforms; fast Fourier transform; sampling, quantization, and aliasing; digital filter design; discrete-time random processes; multirate processing; filter banks and subband decomposition; nonlinear digital filters. Electrical and Computer Engineering 381K (Topic 8) and Electrical Engineering 381K (Topic 8) may not both be counted. Additional prerequisite: Knowledge in probability and random processes, and digital signal processing.

Topic 9: Advanced Signal Processing. Explore signal modeling; optimum filtering; spectral estimation; fast algorithms; and applications in array signal processing, speech coding, and digital communication. Electrical and Computer Engineering 381K (Topic 9) and Electrical Engineering 381K (Topic 9) may not both be counted. Additional prerequisite: Knowledge in probability and random processes, digital signal processing, and matrices and matrix calculations.

Topic 11: Wireless Communications. Introduction to fundamental aspects of wireless communication systems including channel modeling, diversity, multiple antenna transmission and reception, beamforming, adaptive modulation. Explore multiuser communication concepts including OFDMA and SC-FDMA, spread spectrum and CDMA, and broadcast and MAC channels. Examine system-level modeling including network information theory, stochastic geometry, and network interference models. Electrical and Computer Engineering 381K (Topic 11) and Electrical Engineering 381K (Topic 11) may not both be counted. Additional prerequisite: Electrical and Computer Engineering 381K (Topic 2) (or Electrical Engineering 381K (Topic 2)), Electrical and Computer Engineering 471C (or Electrical Engineering 471C) or Electrical and Computer Engineering 381K (Topic 17) (or Electrical Engineering 381K (Topic 17)) or consent of instructor.

Topic 13: Analysis and Design of Communication Networks. Examine stochastic and deterministic traffic and queueing models. Explore techniques for call admission, routing, flow control, network optimization, estimation, and decision making in uncertain environments. Electrical and Computer Engineering 381K (Topic 13) and Electrical Engineering 381K (Topic 13) may not both be counted. Additional prerequisite: Electrical and Computer Engineering 381J (or Electrical Engineering 381J) and 381K (Topic 15) (or Electrical Engineering 381K (Topic 15) or 382N (Topic 5)).

Topic 14: Multidimensional Digital Signal Processing. Explore multidimensional signals and systems, multidimensional discrete Fourier analysis, discrete cosine transform, two-dimensional filters, beamforming, seismic processing, tomography, multidimensional multirate systems, image halftoning, and video processing. Electrical and Computer Engineering 381K (Topic 14) and Electrical Engineering 381K (Topic 14) may not both be counted. Additional prerequisite: Electrical and Computer Engineering 380K (or Electrical Engineering 380K), 381K (Topic 8) (or Electrical Engineering 381K (Topic 8)), or 383P (Topic 1) (or Electrical Engineering 383P (Topic 1)).

Topic 15: Communication Networks: Technology, Architectures, and Protocols. Explore network services and techniques, layered architectures, circuit and packet-switching networks, internetworking, switch architectures, control mechanisms, and economic issues. Only one of the following may be counted: Electrical and Computer Engineering 381K (Topic 15), Electrical Engineering 381K (Topic 15), 382N (Topic 5). Additional prerequisite: Consent of instructor.

Topic 16: Digital Video. Examine video sampling and transform; video (retinal and cortical) filters; motion detection and estimation; statistical models of videos; neuroscience of video perception; natural video statistics; modern video compression standards; video quality prediction; video denoising; active contour models; and video saliency. Only one of the following may be counted: Biomedical Engineering 381J (Topic: Digital Video), 381J (Topic 14), Electrical and Computer Engineering 381K (Topic 16), Electrical Engineering 381K (Topic 16), 381V (Topic: Digital Video). Same as: BME 381J.14

Topic 17: Wireless Communications Laboratory. Explore the fundamentals of wireless communication from a digital signal processing perspective; linear modulation, demodulation, and orthogonal frequency division multiplexing; synchronization, channel estimation, and equalization; communication in fading channels; principles of multiple-input, multiple-output (MIMO) communication; and wireless standards. Only one of the following may be counted: Electrical and Computer Engineering 381K (Topic 17), Electrical Engineering 381K (Topic 17), 381V (Topic: Wireless Communications Lab). Additional prerequisite: Consent of instructor.

Topic 18: Convex Optimization. The fundamentals of convex optimization with a focus on modeling, computation and scale: convex sets and functions, unconstrained optimization via first and second-order methods, duality, constrained optimization, SDPs, stochastic and sub-gradient descent methods, ADMMs, and applications. Only one of the following may be counted: Electrical and Computer Engineering 381K (Topic 18), Electrical Engineering 381K (Topic 18), 381V (Topic: Large Scale Optimization), Operations Research and Industrial Engineering 391Q (Topic 15). Same as: ORI 391Q.15 Additional prerequisite: Consent of instructor.

Topic 19: Wireless Network Modeling and Performance Evaluation. Explore modeling of large wireless networks, random graphs, stochastic geometry, point processes, space and time averages, connectivity, stability, capacity, and network design optimization, with applications for cellular network, mobile ad hoc networks, device to device networks, and vehicular networks. Electrical and Computer Engineering 381K (Topic 19) and Electrical Engineering 381K (Topic 19) may not both be counted. Additional prerequisite: Consent of instructor.

Topic 20: Machine Learning on Real World Networks. Explore networks in natural and socio-technological systems. Examine network structure and dynamics, machine learning on real networks, clustering; network models, small worlds, scale free networks, preferential attachment, community structure, network-on-chip optimization and control, epidemics and rumors, human mobility, network embedding, deep learning on graphs, graph neural networks, and other networks. Electrical and Computer Engineering 381K (Topic 20) and Electrical Engineering 381K (Topic 20) may not both be counted.

Topic 21: Computational Magnetic Resonance Imaging. Introduction to Magnetic Resonance Imaging (MRI), particularly focusing on computational co-design of the signal acquisition and post-processing algorithms. Discuss principles of MRI, including basic system hardware, spin physics, signal formation, contrast mechanisms, pulse sequence design, image detection, and image reconstruction. Examine advanced image reconstruction including parallel imaging, compressed sensing, and machine learning; and exploration of concepts using real and synthetic data. Only one of the following may be counted: Biomedical Engineering 383J (Topic: Computational MRI), Computational Science, Engineering, and Mathematics 397 (Topic: Computational MRI), Electrical and Computer Engineering 381K (Topic 21), Electrical and Computer Engineering 381V (Topic: Computational MRI), Electrical Engineering 381V (Topic: Computational MRI).