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CS 303: Designing Computer Science Experiments

Introduction to empirical research in computer science. Learn how to design, execute, interpret, and report on computer science experiments. Conducting empirical work and using experiments to build theory is one of the major ways to move computer science forward, but these issues are often omitted from computer science curricula. Course features case studies drawn from artificialnnintelligence, systems, and human-computer interaction. Emphasizes thenndecision-making aspects of research and the logic behind researchnnprocedures.
Terms: Spr | Units: 3

CS 309A: Software as a Service

For technology and business students. The shift from traditional software model of disconnected development and CD-ROM deployment to engineering and delivery on the Internet as a service. Guest industry experts give first-hand view of changes in the software industry.
Terms: Aut | Units: 1 | Repeatable for credit
Instructors: Chou, T. (PI)

CS 315A: Parallel Computer Architecture and Programming

The principles and tradeoffs in the design of parallel architectures. Emphasis is on naming, latency, bandwidth, and synchronization in parallel machines. Case studies on shared memory, message passing, data flow, and data parallel machines illustrate techniques. Architectural studies and lectures on techniques for programming parallel computers. Programming assignments on one or more commercial multiprocessors. Prerequisites: EE 282, and reasonable programming experience.
Terms: Spr | Units: 3
Instructors: Olukotun, O. (PI)

CS 319: Topics in Digital 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.
| Repeatable for credit

CS 321: Information Processing for Sensor Networks

Design and implementation of algorithms and protocols for performing information processing tasks in sensor networks, including routing, data dissemination and aggregation, information discovery and brokerage, service establishment (localization, time synchronization), sensor tasking and control, and distributed data storage. Techniques from signal processing, networking, energy-ware computing, distributed databases and algorithms, and embedded systems and platforms. Physical, networking, and application layers and design trade-offs across the layers. Prerequisites: linear algebra and elementary probability, networking background at the level of 144A or EE 284.
Terms: Win | Units: 3
Instructors: Guibas, L. (PI)

CS 322: Network Analylsis

The emergence of the web and large online computing applications can bennseen as a convergence of social and technological networks, with systemsnnsuch as the World Wide Web, blogging platforms and Facebook that can benncharacterized by the interplay between rich information content, thennmillions of individuals and organizations who create it, and thenntechnology that supports it. Course will cover recent research onnnthe structure and analysis of such large social and information networksnnand on models and algorithms that abstract their basic properties.nnTopics: probabilistic models for network structure and evolution,nnmethods for link analysis and network community detection, searchnnalgorithms, diffusion and information propagation on the web, virusnnoutbreak detection in networks, and connections with work in the socialnnsciences and economics. See http://snap.stanford.edu/na09/ for more info.
Terms: Aut | Units: 3
Instructors: Leskovec, J. (PI)

CS 323: Understanding Images and Videos: Recognizing and Learning High-Level Visual Concepts

Field of computer vision has seen an explosive growth in past decade. Much of recent effort in vision research is towards developing algorithms that can perform high-level visual recogniztion tasks on real-world images and videos. With development of Internet, this task becomes particularly challenging and interesting given the heterogeneous data on the web. Course will focus on reading recent research papers that are focused on solving high-level visual recognition problems, such as object recognition and categorization, scene understanding, human motion understanding, etc. Project required. Prerequisite: some experience in research with one of the following fields: computer vision, image processing, computer graphics, machine learning.
Terms: Aut | Units: 3
Instructors: Li, F. (PI)

CS 324: Experimental Robotics, Perception for Manipulation

Hands-on project course on robotic perception (2D and 3D sensing primarily) for the purposes of manipulating objects. Topics: Review of robotics control, planning and grasping; review of computer vision with more depth in object recognition and tracking. Detailed instruction on 3D sensing, 3D signal processing, 3D features, model registration, mesh generation, pose recognition. Pragmatic machine learning review and instruction in software tools in these areas as preparation for project work on real robots with rich 2D and 3D sensing abilities. Limited enrollment. Prerequisites: CS223A, CS223B.
Terms: Spr | Units: 3

CS 327A: Advanced Robotics

Emerging areas of human-centered robotics and interactive haptic simulation of virtual environments. Topics: redundancy; task-oriented dynamics and control, whole-body control-task and posture decomposition, cooperative robots, haptics and simulation, haptically augmented teleoperation, human-friendly robot design. Prerequisites: 223A or equivalent.
Terms: Spr | Units: 3
Instructors: Khatib, O. (PI)

CS 329: Topics in Artificial Intelligence

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.
| Repeatable for credit
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