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  • CS 144: Ideas behind the Web

    9.00 Credits
    California Institute of Technology

    The Web is an essential part of our lives, and we all depend on it every day, but do you really know what makes it work? This course studies the “big” ideas behind the Web: How do search engines work? How can search engines make so much money from putting ads next to their search results? Are there ways to prevent spammers from accumulating lots of e-mail addresses? What does the Web actually look like? How big is the Web? For all these questions and more, the course will provide a mixture of both mathematical models and real-world, hands-on labs. Instructor: Wierman. Prerequisite:    Ma 2 ab, CS 24 and CS 38, or instructor permission.
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  • CS 145: Projects in Networking

    9.00 Credits
    California Institute of Technology

    Students are expected to execute a substantial project in networking, write up a report describing their work, and make a presentation. Instructors: Chandy, Low, Wierman. Prerequisite:    Either CS/EE 144 or CS 141 b in the preceding term, or instructor permission.
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  • CS 146: Advanced Networking

    9.00 Credits
    California Institute of Technology

    This is a research-oriented course meant for undergraduates and beginning graduate students who want to learn about current research topics in networks such as the Internet, power networks, social networks, etc. The topics covered in the course will vary, but will be pulled from current research topics in the design, analysis, control, and optimization of networks, protocols, and Internet applications. Usually offered in alternate years. Not offered 2012-13. Prerequisite:    CS/EE 143 or instructor’s permission.
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  • CS 147: Network Performance Analysis

    9.00 Credits
    California Institute of Technology

    When designing a network protocol, distributed system, etc., it is essential to be able to quantify the performance impacts of design choices along the way. For example, should we invest in more buffer space or a faster processor? One fast disk or multiple slower disks? How should requests be scheduled? What dispatching policy will work best? Ideally, one would like to make these choices before investing the time and money to build a system. This class will teach students how to answer this type of “what if” question by introducing students to analytic performance modeling, the tools necessary for rigorous system design. The course will focus on the mathematical tools of performance analysis (which include stochastic modeling, scheduling theory, and queueing theory) but will also highlight applications of these tools to real systems. Usually offered in alternate years. Instructor: Wierman.
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  • CS 148 ab: Selected Topics in Computational Vision

    9.00 Credits
    California Institute of Technology

    The class will focus on an advanced topic in computational vision: recognition, vision-based navigation, 3-D reconstruction. The class will include a tutorial introduction to the topic, an exploration of relevant recent literature, and a project involving the design, implementation, and testing of a vision system. Instructor: Perona. Part a not offered 2012–13; Part b offered 2012–13.
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  • CS 150: Probability and Algorithms

    9.00 Credits
    California Institute of Technology

    Elementary randomized algorithms and algebraic bounds in communication, hashing, and identity testing. Game tree evaluation. Topics may include randomized parallel computation; independence, k-wise independence and derandomization; rapidly mixing Markov chains; expander graphs and their applications; clustering algorithms. Not offered 2012–13. Prerequisite:    CS 38 a and Ma 5 abc.
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  • CS 151: Complexity Theory

    12.00 Credits
    California Institute of Technology

    This course describes a diverse array of complexity classes that are used to classify problems according to the computational resources (such as time, space, randomness, or parallelism) required for their solution. The course examines problems whose fundamental nature is exposed by this framework, the known relationships between complexity classes, and the numerous open problems in the area. Instructor: Umans. Prerequisite:    CS 21 and CS 38, or instructor’s permission.
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  • CS 153: Current Topics in Theoretical Computer Science

    9.00 Credits
    California Institute of Technology

    May be repeated for credit, with permission of the instructor. Students in this course will study an area of current interest in theoretical computer science. The lectures will cover relevant background material at an advanced level and present results from selected recent papers within that year’s chosen theme. Students will be expected to read and present a research paper. Instructor: Schulman. Prerequisite:    CS 21 and CS 38, or instructor’s permission.
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  • CS 154: Artificial Intelligence

    9.00 Credits
    California Institute of Technology

    How can we build systems that perform well in unk nown environments and unforeseen situations? How can we develop systems that exhibit “intelligent” behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students. Not offered 2012–13. Prerequisite:    Ma 2 b or equivalent, and CS 1 or equivalent.
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  • CS 155: Probabilistic Graphical Models

    9.00 Credits
    California Institute of Technology

    Many real-world problems in AI, computer vision, robotics, computer systems, computational neuroscience, computational biology, and natural language processing require one to reason about highly uncertain, structured data, and draw global insight from local observations. Probabilistic graphical models allow addressing these challenges in a unified framework. These models generalize approaches such as hidden Markov models and Kalman filters, factor analysis, and Markov random fields. In this course, we will study the problem of learning such models from data, performing inference (both exact and approximate), and using these models for making decisions. The techniques draw from statistics, algorithms, and discrete and convex optimization. The course will be heavily research- oriented, covering current developments such as probabilistic relational models, models for naturally combining logical and probabilistic inference, and nonparametric Bayesian methods. Not offered 2012–13.
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