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81 - 90 of 215 results for: CS

CS 272: Introduction to Biomedical Informatics Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212)

Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 211 or 214 or 217 or consent of instructor.
Terms: Spr | Units: 3 | Grading: Medical Option (Med-Ltr-CR/NC)
Instructors: Altman, R. (PI)

CS 273A: A Computational Tour of the Human Genome (BIOMEDIN 273A, DBIO 273A)

Introduction to computational biology through an informatic exploration of the human genome. Topics include: genome sequencing (technologies, assembly, personalized sequencing); functional landscape (genes, gene regulation, repeats, RNA genes, epigenetics); genome evolution (comparative genomics, ultraconservation, co-option). Additional topics may include population genetics, personalized genomics, and ancient DNA. Course includes primers on molecular biology, the UCSC Genome Browser, and text processing languages. Guest lectures from genomic researchers. No prerequisites. See http://cs273a.stanford.edu/.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 276: Information Retrieval and Web Search (LINGUIST 286)

Text information retrieval systems; efficient text indexing; Boolean, vector space, and probabilistic retrieval models; ranking and rank aggregation; evaluating IR systems. Text clustering and classification: classification algorithms, latent semantic indexing, taxonomy induction; Web search engines including crawling and indexing, link-based algorithms, and web metadata. Prerequisites: CS 107, CS 109, CS 161.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

CS 298: Seminar on Teaching Introductory Computer Science (EDUC 298)

Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Terms: Aut | Units: 1 | Grading: Satisfactory/No Credit

CS 309A: Cloud Computing

For science, engineering, business, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. This class is intended for all students who want to begin to understand the implications of this shift in technology. Guest industry experts are public company CEOs who are delivering application, software development, operations management, compute, storage & data center, and network cloud services.
Terms: Aut | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Chou, T. (PI)

CS 316: Advanced Multi-Core Systems (EE 382E)

In-depth coverage of the architectural techniques used in modern, multi-core chips for mobile and server systems. Advanced processor design techniques (superscalar cores, VLIW cores, multi-threaded cores, energy-efficient cores), cache coherence, memory consistency, vector processors, graphics processors, heterogeneous processors, and hardware support for security and parallel programming. Students will become familiar with complex trade-offs between performance-power-complexity and hardware-software interactions. A central part of CS316 is a project on an open research question on multi-core technologies. Prerequisites: EE 108B. Recommended: CS 149, EE 282.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 323: Automated Reasoning: Theory and Applications

Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications.
Terms: Spr | Units: 3-4 | Grading: Letter or Credit/No Credit
Instructors: Ermon, S. (PI)

CS 334A: Convex Optimization I (CME 364A, EE 364A)

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.
Terms: Win, Sum | Units: 3 | Grading: Letter or Credit/No Credit

CS 341: Project in Mining Massive Data Sets

Team project in data-mining of very large-scale data, including the problem statement and implementation and evaluation of a solution; some lectures on relevant materials will be given: Hadoop, Hive, Amazon EC2; other topics of possible relevance to some projects: computational advertising and the adwords problem; graph partitioning and community detection; extracting relations from the Web; stream data processing.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit

CS 344G: (Your) Great Ideas for Networked Applications

Graduate project class on computer networking, emphasizing end-to-end applications and protocols. Students will propose and execute an original project in teams of 2-3, culminating in a final writeup and presentation/demonstration. Each week, students will read, present, and lead a discussion about a seminal paper or system. Prerequisites: programming experience; CS 244 recommended but not required.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Winstein, K. (PI)
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