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Course Criteria
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1.00 Credits
Explores the visual and human-computer interaction design process for scientific applications in Brown's immersive virtual reality Cave. Joint with RISD. Computer Science and design students learn how to work together effectively; study the process of design; learn about scientific problems; create designs applications; critique, evaluate, realize and iterate designs; and demonstrate final projects. Instructor permission required.
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1.00 Credits
Explores the fundamental principles and practice underlying networked information systems, first we cover basic distributed computing mechanisms (e.g., naming, replication, security, etc.) and enabling middleware technologies. We then discuss how these mechanisms and technologies fit together to realize distributed databases and file systems, web-based and mobile information systems. Prerequisite: CSCI 0320 or 0360.
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1.00 Credits
Theoretical and practical approaches to designing intelligent systems. Example tasks range from game playing to hardware verification. Core topics include knowledge representation, search and optimization, and automated reasoning. Application areas include natural language processing, machine vision, machine learning, and robotics. Strongly recommended: CSCI 0160, CSCI 0180 or CSCI 0190; and CSCI 0220.
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1.00 Credits
How can we program computers to understand the visual world? This course treats vision as inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. Topics may include perception of 3D scene structure from stereo, motion, and shading; segmentation and grouping; texture analysis; learning, object recognition; tracking and motion estimation. Strongly recommended: basic linear algebra, calculus, and probability.
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1.00 Credits
Introduction to computational linguistics (also known as natural-language processing) including the related mathematics and several programming projects. Particular topics include: language modeling (as used in e.g., speech recognition, machine translation), machine translation, part-of-speech labeling, syntactic parsing, and pronouns resolution. Mathematical techniques include basic probability, noisy channel models, the EM (Expectation-Maximization) algorithm, hidden Markov models, probabilistic context-free grammars, and the forward-backward algorithm. Prerequisites: CSCI 1410 or instructor permission, which will be given to all students with a solid background in programming and either basic probability, or enough mathematical background to quickly absorb the latter. Not open to first year students.
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1.00 Credits
How do robots function autonomously in dynamic, unpredictable environments? This course focuses on programming mobile robots, such as the iRobot Roomba, to perceive and act autonomously in real-world environments. The major paradigms for autonomous control and robot perception are examined and compared with robotic notions in science fiction. Prerequisite: CSCI 0150, CSCI 0170 or CSCI 0190. Recommended: CSCI 1410 or CSCI 1230.
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1.00 Credits
This course covers the algorithmic aspects of optimizing decisions in fully observable, non-changing environments. Students are introduced to state-of-the-art optimization methods such as linear programming, integer programming, local search, and constraint programming. Strongly recommended: CSCI 0160, CSCI 0180 or CSCI 0190; CSCI 0510; and CSCI 0530 or MATH 0520 or MATH 0540.
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1.00 Credits
This course studies the tools for guaranteeing safe communication and computation in an adversarial setting. We develop notions of security and give provably secure constructions for such cryptographic objects as cryptosystems, signature schemes and pseudorandom generators. We also review the principles for secure system design. Prerequisites: CSCI 0220 and CSCI 0510.
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1.00 Credits
Introduction to probability theory in computer science, in particular randomized algorithms and probabilistic analysis of algorithms. Introduces basic probability theory and presents applications of randomized and probabilistic analysis techniques in areas such as combinatorial optimization, data structures, communication, and parallel computation. Assumes no prior knowledge of probability theory. Prerequisite: CSCI 0220 or equivalent. CSCI 1570 recommended but not required.
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1.00 Credits
A single algorithmic improvement can have a greater impact on our ability to solve a problem than ten years of incremental improvements in CPU speed. We study techniques for designing and analyzing algorithms. Typical problem areas addressed include numerical computing, hashing, searching, dynamic programming, graph algorithms, network flow, and string parsing and matching. Prerequisites: CSCI 0160, CSCI 0180, or CSCI 0190, and CSCI 0220.
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