printer friendly pageCS 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 358: Topics in Programming Language Theory
Topics of current research interest in the mathematical analysis of programming languages, structured operational semantics, domain theory, semantics of concurrency, rich type disciplines, problems of representation independence, and full abstraction. See Time Schedule or Axess for current topics. May be repeated for credit. Prerequisites: 154, 157, 258, or equivalents. (Staff)
| Repeatable
for credit
CS 359: Topics in the Theory of Computation
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: Spring 2005
| Repeatable
for credit
CS 369: Topics in Analysis of Algorithms
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: Spring 2013
| Repeatable
for credit
CS 369E: Topics in Analysis of Algorithms: Communication Complexity (for Algorithm Designers)
Fundamentals of communication complexity, with a strong emphasis on applications to proving lower bounds in important computational models. Application areas include data stream algorithms, data structures, extended formulations of linear programs, and combinatorial auctions.
Terms: Win
| Units: 3
Instructors:
Roughgarden, T. (PI)
CS 371: Computational Biology in Four Dimensions (BIOMEDIN 371, BIOPHYS 371, CME 371)
Computational approaches to understanding the three-dimensional spatial organization of biological systems and how that organization evolves over time. The course will cover cutting-edge research in both physics-based simulation and computational analysis of experimental data, at scales ranging from individual molecules to entire cells. Prerequisite:
CS 106A or equivalent, and an introductory course in biology or biochemistry. Recommended: some experience in mathematical modeling (does not need to be a formal course).
Terms: Spr
| Units: 3
CS 373: Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, GENE 245, STATS 345)
Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Terms: Spr
| Units: 2-3
CS 374: Algorithms in Biology (BIOMEDIN 374)
Algorithms and computational models applied to molecular biology and genetics. Topics vary annually. Possible topics include biological sequence comparison, annotation of genes and other functional elements, molecular evolution, genome rearrangements, microarrays and gene regulation, protein folding and classification, molecular docking, RNA secondary structure, DNA computing, and self-assembly. May be repeated for credit. Prerequisites: 161, 262 or 274, or BIOCHEM 218, or equivalents.
Terms: Spr
| Units: 2-3
Instructors:
Batzoglou, S. (PI)
CS 376: Human-Computer Interaction Research
Prepares students to conduct original HCI research by reading and discussing seminal and cutting-edge research papers. Main topics are ubiquitous computing, social computing, and design and creation; breadth topics include HCI methods, programming, visualization, and user modeling. Student pairs perform a quarter-long research project. Prerequisites: For CS and Symbolic Systems undergraduates/masters students,
CS 147 or
CS 247.No prerequisite for PhD students or students outside of CS and Symbolic Systems.
Terms: Spr
| Units: 3-4
| Repeatable
for credit
Instructors:
Bernstein, M. (PI)
CS 377: Topics in Human-Computer Interaction
Contents change each quarter. May be repeated for credit. See
http://hci.stanford.edu/academics for offerings.
| Repeatable
for credit
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