Brand New ECE Graduate Courses Starting January 2020
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December 17, 2019
Brand New ECE Graduate Courses Starting January 2020
The Department of Electrical and Computer Engineering is excited to announce the start of 3 different courses starting in Winter 2020 at UBC! The courses include ELEC 503: Integrated Circuits for High-Speed Data Links, EECE 571W: Mathematical Data Science, EECE 571U: Advanced Topics in Systems and Control and EECE 571S: Introduction to Quantum Computing.
ELEC 503: Integrated Circuits for High-Speed Data Links
Instructor: Sudip Shekhar
Credits: 3
Register here
Background: High-speed Input/Output (I/O) data links are used to communicatee between microprocessor, memory, hard drive, external plug-and-play devices, etc. With the exponential rise in the aggregate data demand over thee last decade, design of high-speed links have become challenging. An I/O designeer neeeds to have an understanding of the data channel, communication and signaling schemes, transmit, receive and clocking circuits, and overall link analysis and optimization. Tis demands a knowledge of analog circuits, digital circuits, mixed-signal circuits, microwave and electromagnetism, and data communications. The students will learn about all these techniques and will obtain the ability to implement, validate and assess such techniques in this course.
Objective: To understand a variety of signaling schemes and system-level trade-offs in high-speed digital links. To design and simulate both analog and digital circuits for high-speed links using Cadence and MATLAB. To become familiar with different link standards such as PCIExpress, SATA, DDR, etc. To obtain an overview of the state-of-the-art in high-speed I/O links.
Topics: Introduction to high-speed links, noise and Jutter, characterizing data channels in frequency and time domain, equalization, link modeling and simulation, signaling schemes, design of different transmitter circuits, design of different receiver circuits, different clocking schemes, clock and data recovery, synchromnization.
More info here
EECE 571S: Introduction to Quantum Computing
Instructor: Joseph Salfi
Credits: 3
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Background: In the past two decades scientists have begun to formulate and build a new type of computer called a quantum computer. Immense gains in computational power have been predicted to be possible with these kinds of computers, and the first commercial quantum computers are starting to appear, albeit with limited capabilities. This class will teach students the fundamental aspects and applications of quantum computers.
Objective: The course will begin by understanding the postulates of quantum mechanics and the matrix framework of quantum information science. This will expose some of the most bizarre concepts in quantum theory that are actually crucial to the operation of quantum computers: the uncertainty principle, quantum measurement, entanglement, and spooky action at a distance. We will apply this framework to study quantum circuits, quantum algorithms, the physical realization of quantum computers, and quantum error correction, and we will learn how to program quantum computers.
Topics: Linear algebra, quantum mechanics, measurement, multi-qubit states, uncertainty relations, entanglement, quantum gates and circuits, quantum Fourier transform and its applications in phase estimation, order finding, quantum search, alternative models for quantum computing annealing, decoherence, physical implementation of quantum computers, spin-based quantum computers and quantum error correction.
EECE 571U: Advanced Topics in Systems and Control
Instructor: Maryam Kamgarpour
Credits: 3
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Objective: To develop an in-depth understanding of mathematical tools for cutting-edge research at the intersection of control and machine learning and to apply these tools in a project of own’s choosing. We will start with fundamentals of linear dynamical systems solutions, stability and optimal control. We will then move on to optimization and optimal control for uncertain and multi-agent systems. The course material is based on established books for fundamental topics and recent literature for cutting-edge topics.
Topics: Linear systems theory: fundamentals of linear and Hilbert space theory for characterizing solutions of dynamical systems and for stability, controllability/observability analysis; optimal control theory: stochastic systems, dynamic programming, approximate dynamic programming; learning & control: online optimization, safe learning, game theory and multi-agent learning.
EECE 571W: Mathematical Data Science
Instructor: Lele Wang
Credits: 3
Register here
Background: A large variety of data science and machine learning problems use graphs to characterize structural properties of the data. In social networks, graphs represent friendship among users. In biological networks, graphs indicate protein interactions. In World Wide Web, graphs describe hyper-links between web pages. In recommendation systems, graphs reveal the economic behaviors of users. Unlike the one-dimensional linear data sequence, data appearing in the form of a graph can be viewed as a two-dimensional matrix with special structures. How to compress, store, process, estimate, predict, and learn such large-scale structural information are important new challenges in data science.
Objective: To provide an introduction to mathematical and algorithmic tools for studying such problems. Both information-theoretic methods for determining the fundamental limits as well as methodologies for attaining these limits will be discussed. The course aims to expose students to the state- of-the-art research in mathematical data science, statistical inference on graphs, combinatorial statistics, among others, and prepare them with related research skills.
Topics: Random graphs, tools from the probabilistic method, evolution, vertex degrees, connectivity, small subgraphs, spanning subgraphs, extreme characteristics, spectral method and applications including community detection, graph matching, sorting and ranking.
