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141 - 150 of 310 results for: CS

CS 238: Decision Making under Uncertainty (AA 228)

This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. Prerequisites: basic probability and fluency in a high-level programming language.
Terms: Aut | Units: 3-4

CS 239: Advanced Topics in Sequential Decision Making (AA 229)

Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic. Prerequisites: AA 228/ CS 238 or CS 221.
Last offered: Winter 2020

CS 240: Advanced Topics in Operating Systems

Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming. Prerequisite: 140 or equivalent.
Terms: Spr | Units: 3 | Repeatable for credit
Instructors: Engler, D. (PI)

CS 240LX: Advanced Systems Laboratory, Accelerated

This is an implementation-heavy, lab-based class that covers similar topics as CS240, but by writing code versus discussing papers. Our code will run "bare-metal" (without an operating system) on the widely-used ARM-based raspberry pi. Bare-metal lets us do interesting tricks without constantly fighting a lumbering, general-purpose OS that cannot get out of its own way. We will do ten projects, one per week, where each project covers two labs of (at a minimum) several hours each and a non-trivial amount of outside work. The workload is significant, but I will aim to not waste your time. Prerequisite: CS140E or instructor permission.
Last offered: Spring 2020

CS 241: Embedded Systems Workshop (EE 285)

Project-centric building hardware and software for embedded computing systems. Students work on an existing project of their own or join one of these projects. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, deterministic timing and synchronization, state-based programming models, filters, frequency response, and high-frequency signals, low power operation, system and PCB design, security, and networked communication. Prerequisite: CS107 (or equivalent).
Terms: Win | Units: 3 | Repeatable 3 times (up to 9 units total)

CS 242: Programming Languages

This course explores models of computation, both old, like functional programming with the lambda calculus (circa 1930), and new, like memory-safe systems programming with Rust (circa 2010). Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, and the semantic gap between computational models and modern hardware. The study of programming languages is equal parts systems and theory, looking at how a rigorous understanding of the syntax, structure, and semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. In light of today's Cambrian explosion of new programming languages, this course also seeks to provide a conceptual clarity on how to compare and contrast the multitude of programming languages, models, and paradigms in the modern programming landscape. Prerequisites: 103, 110.
Terms: Win | Units: 3-4
Instructors: Aiken, A. (PI)

CS 243: Program Analysis and Optimizations

Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. The methodology of applying mathematical abstractions such as graphs, fixpoint computations, binary decision diagrams in writing complex software, using compilers as an example. Topics include data flow analysis, instruction scheduling, register allocation, parallelism, data locality, interprocedural analysis, and garbage collection. Prerequisites: 103 or 103B, and 107.
Terms: Win | Units: 3-4
Instructors: Lam, M. (PI)

CS 244: Advanced Topics in Networking

Classic papers, new ideas, and research papers in networking. Architectural principles: why the Internet was designed this way? Congestion control. Wireless and mobility; software-defined networks (SDN) and network virtualization; content distribution networks; packet switching; data-center networks. Prerequisite: 144 or equivalent.
Terms: Spr | Units: 3-4

CS 244B: Distributed Systems

Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security. Prerequisites: CS 144.
Last offered: Spring 2020

CS 245: Principles of Data-Intensive Systems

Most important computer applications have to reliably manage and manipulate datasets. This course covers the architecture of modern data storage and processing systems, including relational databases, cluster computing frameworks, streaming systems and machine learning systems. Topics include storage management, query optimization, transactions, concurrency, fault recovery, and parallel processing, with a focus on the key design ideas shared across many types of data-intensive systems. Prerequisites: CS 145, 161.
Terms: Win | Units: 3
Instructors: Zaharia, M. (PI)
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