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  COVID-19 Scheduling Updates!
Due to recent announcements about Autumn Quarter (see the President's update), please expect ongoing changes to the class schedule.

231 - 240 of 310 results for: CS

CS 348I: Computer Graphics in the Era of AI

This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and computational photography. We will study a wide range of problems on content creation for images, shapes, and animations, recently advanced by deep learning techniques. For each problem, we will understand its conventional solutions, study the state-of-the-art learning-based approaches, and critically evaluate their results as well as the impacts to researchers and practitioners in Computer Graphics. The topics include differentiable rendering/neural rendering, BRDF estimation, texture synthesis, denoising, procedural modeling, mesh segmentation, view prediction, colorization, style transfer, sketch simplification, character animation, physics simulation, and facial animation. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, to apply neural procedural modeling for shape and scene synthesis, to implement policy learning algorithms for creating character animation, and to exploit data-driven methods for simulating physical phenomena. Recommended Prerequisites: CS248, CS231N, CS229, CS205A.
Terms: Aut | Units: 3

CS 348K: Visual Computing Systems

Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compet more »
Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project. Prerequisites: CS 107 or equivalent. Highly recommended: Parallel Computing ( CS149) or Computer Architecture ( EE 282). Students will benefit from some background in deep learning ( CS 230, CS 231N), computer vision ( CS 231A), digital image processing ( CS 232) or computer graphics ( CS248).
Terms: Spr | Units: 3-4

CS 349: Topics in Programming Systems

Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
Last offered: Winter 2006 | Repeatable for credit

CS 349D: Cloud Computing Technology

The largest change in the computer industry over the past five years has arguably been the emergence of cloud computing: organizations are increasingly moving their workloads to managed public clouds and using new, global-scale services that were simply not possible in private datacenters. However, both building and using cloud systems remains a black art with many difficult research challenges. This research seminar will cover industry and academic work on cloud computing and survey challenges including programming interfaces, cloud native applications, resource management, pricing, availability and reliability, privacy and security. Students will also propose and develop an original research project.n nPrerequisites: For graduate students, background in computer systems ( CS 240, 244, 244B or 245) is strongly recommended. Undergrads will need instructor's approval.
Last offered: Autumn 2018

CS 349F: Technology for Financial Systems

Financial systems have spurred technological innovation and, in turn, are driven byncutting-edge technological developments. This course explores the synergy.nStudents will learn from faculty and industry experts how to build faster and fairer financial systems. Topics include network infrastructure: data center fabrics, ultra-low latency trading systems; cloud computing infrastructure: building large-scale risk computation platforms using virtual machines, containers and serverless computing. A particular focus will be on challenges and opportunities presented by cloud-native financial exchanges: the course will provide such an exchange and student groups will write programs for high-frequency and algorithmic trading. Recommended: Knowledge of basic Networking, OS, or Distributed Systems ( CS 144, 140, or equivalent), as well as basic EE courses ( EE 178) will be useful.
Terms: Aut | Units: 2

CS 349G: Selected Reading of Ph.D. Dissertations

Detailed reading of 5 selected Ph.D. dissertations within a field of computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to read dissertations and discuss their strengths and weaknesses. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each dissertation completes with a guest lecture by its author. The selected dissertations change with each offering but are always from a coherent time period and topic area. Prerequisites: CS110 for undergraduates, EE282 for graduate students.
Terms: Win | Units: 3 | Repeatable 10 times (up to 30 units total)

CS 349T: Project Lab: Video and Audio Technology for Live Theater in the Age of COVID (EE 192T)

This class is part of a multi-disciplinary collaboration between researchers in the CS, EE, and TAPS departments to design and develop a system to host a live theatrical production that will take place over the Internet in the winter quarter. The performing arts have been greatly affected by a transition to theater over Zoom and its competitors, none of which are great at delivering low-latency audio to actors, or high-quality audio and video to the audience, or feedback from the audience back to actors. These are big technical challenges. During the fall, we'll build a system that improves on current systems in certain areas: audio quality and latency over spotty Internet connections, video quality and realistic composited scenes with multiple actors, audience feedback, and perhaps digital puppetry. Students will learn to be part of a deadline-driven software development effort working to meet the needs of a theater director and creative specialists -- while communicating the effect o more »
This class is part of a multi-disciplinary collaboration between researchers in the CS, EE, and TAPS departments to design and develop a system to host a live theatrical production that will take place over the Internet in the winter quarter. The performing arts have been greatly affected by a transition to theater over Zoom and its competitors, none of which are great at delivering low-latency audio to actors, or high-quality audio and video to the audience, or feedback from the audience back to actors. These are big technical challenges. During the fall, we'll build a system that improves on current systems in certain areas: audio quality and latency over spotty Internet connections, video quality and realistic composited scenes with multiple actors, audience feedback, and perhaps digital puppetry. Students will learn to be part of a deadline-driven software development effort working to meet the needs of a theater director and creative specialists -- while communicating the effect of resource limits and constraints to a nontechnical audience. This is an experimental hands-on laboratory class, and our direction may shift as the creative needs of the theatrical production evolve. Based on the success of class projects and subsequent needs, some students may be invited to continue in the winter term with a research appointment (for pay or credit) to operate the system you have built and instruct actors and creative professionals how to work with the system through rehearsals and the final performance before spring break. Prerequisites: CS110 or EE102A. Recommended: familiarity with Linux, C++, and Git.
Terms: Aut | Units: 3

CS 350: Secure Compilation

This course explores the field of secure compilation, which sits at the intersection between security and programming languages. The course covers the following topics: threat models for secure compilers, formal criteria for secure compilers to adhere to, security relevance of secure compilation criteria, security architectures employed to achieve secure compilation, proof techniques for secure compilation with a focus on backtranslation.
Terms: Spr | Units: 3

CS 351: Open Problems in Coding Theory

Coding theory is the study of how to encode data to protect it from noise. Coding theory touches CS, EE, math, and many other areas, and there are exciting open problems at all of these frontiers. In this class, we will explore these open problems by reading recent research papers and thinking about some open problems together. Required work will involve reading and presenting research papers, as well as working in small groups at these open problems and presenting progress. (Solving an open problem is not required!) Topics will depend on student interest and may include locality, coded computation, index coding, interactive communication, and group testing. Prerequisites: CS250 / EE387 or EE388; or linear algebra and permission of the instructor.
Terms: Spr | Units: 3
Instructors: Wootters, M. (PI)

CS 352: Pseudo-Randomness

Pseudorandomness is the widely applicable theory of efficiently generating objects that look random, despite being constructed using little or no randomness. Since psudorandom objects can replace uniformly distributed ones (in a well-defined sense), one may view pseudorandomness as an extension of our understanding of randomness through the computational lens. We will study the basic tools pseudorandomness, such as limited independence, randomness extractors, expander graphs, and pseudorandom generators. We will also discuss the applications of pseudrandomness to derandomization, cryptography and more. We will cover classic result as well as cutting-edge techniques. Prerequisites: CS 154 and CS 161, or equivalents.
Last offered: Spring 2017
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