printer friendly pageCS 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.
Terms: Win
| Units: 3-4
Instructors:
Kochenderfer, M. (PI)
;
Jamgochian, A. (TA)
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.
Last offered: Spring 2023
| Repeatable
for credit
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.
Terms: Spr
| Units: 3
Instructors:
Engler, D. (PI)
CS 241: Embedded Systems Workshop (EE 285)
Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation. Prerequisites: one of
CS107,
EE101A,
EE108,
ME80.
Terms: Win
| Units: 3
| Repeatable
3 times
(up to 9 units total)
Instructors:
Levis, P. (PI)
CS 242: Programming Languages
This course explores foundational models of computation, such as the lambda calculus and other small calculi, and the incorporation of basic advances in PL theory into modern programming languages such as Haskell and Rust. Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, verification, 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 semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems. Prerequisites: 103, 110.
Terms: Aut
| Units: 3-4
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
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.
Terms: Spr
| Units: 3
Instructors:
Humphries, J. (PI)
;
Mazieres, D. (PI)
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.
Last offered: Winter 2022
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