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Course Criteria
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1.00 Credits
Covers the practical aspects involved in designing, writing, tuning, and debugging software designed to run on parallel and distributed systems. Topics might include client-server computation, threads, networks of workstations, message passing, shared memory, partitioning strategies, load balancing, algorithms, remote procedure call, and synchronization techniques. Prerequisites: CS 22 and either 32 or 36; 51 recommended.
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1.00 Credits
Processing molecular biology data (DNA, RNA, proteins) has become central to biological research and a challenge for science research. Important objectives are molecular sequence analysis, recognition of genes and regulatory elements, molecular evolution, protein structure, comparative genomics. This course models the underlying biology in the terms of computer science and presents the most significant algorithms of molecular computational biology. Prerequisites: CSCI 0160, CSCI 0180 or CSCI 0190, and CSCI 0220, or consent of instructor.
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1.00 Credits
No description available.
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1.00 Credits
No description available.
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1.00 Credits
Students identify, design, and implement significant software applications and learn and practice techniques of project management, requirements, specification, analysis, design, coding, documentation, testing, maintenance, and communication. Prerequisite: CSCI 0320 or CSCI 0360.
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1.00 Credits
First-semester course in various branches of computer science. Specific topics to be determined at the beginning of each semester.
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1.00 Credits
In this course you will learn how to apply tools from statistics and computer science to build computational models of physical and biological systems. Example applications include modeling and then simulating the behavior of a collection of genes, the spread of disease in a population, a single neuron in isolation or the complex of neurons comprising the primate visual cortex.
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1.00 Credits
This course will investigate (through a mixture od lectures and student presentations of recent papers) topics in computational topology, including Morse theory and discrete differential geometry. Other possible topics are knot polyonmials, simplicial homology, and geometric probability theory. Some mathematical sophistication and programming skills required. No prerequisites.
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1.00 Credits
This workshop will explore advanced tools and techniques for the creation of innovative and expressive works of digital art. Lectures will address the application of best practices from the software design community to the context of artistic practice. In the first section of the course, students will exercise their aesthetic, conceptual, and technical skills on a set of 'mini-projects' exploring the analysis, generation and presentation of computationally-augmented literary texts. Assignments will include web-text mining, feature extraction, grammars, generative algorithms, and statistical techniques. During the second half of the course, students will focus on a larger work of their own design, participating in regular critiques throughout the development cycle. Though assignments will focus on digital literature, a wide range of artistic media will be explored, including sound, image, video, 3D and installation. Collaboration is encouraged. Instructor permission required. Enrollment limited to 18.
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1.00 Credits
How can artificial systems learn from examples, and discover information buried in massive datasets? This course explores the theory and practice of statistical machine learning. Topics include parameter estimation, probabilistic graphical models, approximate inference, and kernal and nonparametric methods. Applications to regression, categorization, and clustering problems are illustrated by examples from vision, language, communications, and bioinformatics. Prerequisites: CSCI 0160, 0180, or 0190, and comfort with basic probability, linear algebra, and calculus.
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