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221 - 230 of 241 results for: CS

CS 424M: Learning Analytics and Computational Modeling in Social Science (EDUC 390)

Computational modeling and data-mining are dramatically changing the physical sciences, and more recently also the social and behavioral sciences. Traditional analysis techniques are insufficient to investigate complex dynamic social phenomena as social networks, online gaming, diffusion of innovation, opinion dynamics, classroom behavior, and other complex adaptive systems. In this course, we will learn about how modeling, network theory, and basic data-mining can support research in cognitive, and social sciences, in particular around issues of learning, cognitive development, and educational policy.
Terms: not given this year, last offered Winter 2013 | Units: 3-4 | Grading: Letter or Credit/No Credit

CS 428: Computation and Cognition: The Probabilistic Approach (PSYCH 204)

This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit

CS 431: High-level Vision: From Neurons to Deep Neural Networks (PSYCH 250)

Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, and Computer Science. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit

CS 448: Topics in Computer Graphics

Topic changes each quarter. Recent topics: computational photography, datanvisualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offererings and prerequisites. May be repeated for credit.
Terms: not given this year, last offered Autumn 2007 | Units: 3-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 448B: Data Visualization

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project. Prerequisite: one of 147, 148, or equivalent.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 448H: Topics in Computer Graphics: Agile Hardware Design

Topic changes each quarter. Recent topics: computational photography, data visualization, character animation, virtual worlds, graphics architectures, advanced rendering. See http://graphics.stanford.edu/courses for offerings and prerequisites. May be repeated for credit.
Terms: not given this year, last offered Winter 2018 | Units: 3 | Grading: Letter or Credit/No Credit

CS 448I: Computational Imaging and Display (EE 367)

Spawned by rapid advances in optical fabrication and digital processing power, a new generation of imaging technology is emerging: computational cameras at the convergence of applied mathematics, optics, and high-performance computing. Similar trends are observed for modern displays pushing the boundaries of resolution, contrast, 3D capabilities, and immersive experiences through the co-design of optics, electronics, and computation. This course serves as an introduction to the emerging field of computational imaging and displays. Students will learn to master bits and photons.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit

CS 468: Topics in Geometric Algorithms: Machine Learning for 3D Data

Contents of this course change with each offering. Past offerings have included geometric matching, surface reconstruction, collision detection, computational topology. May be repeated for credit. Winter 2013/14 iteration will cover Computational Symmetry & Regularity and spring quarter 2013/14 will cover data-driven shape analysis. Prerequisites: Math 52 or equivalent, basic coding.
Terms: not given this year, last offered Spring 2017 | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

CS 476A: Music, Computing, Design I: The Art of Design (MUSIC 256A)

Creative design for computer music software. Programming, audiovisual design, as well as software design for musical tools, instruments, toys, and games. Provides paradigms and strategies for designing and building music software, with emphases on interactive systems, aesthetics, and artful product design. Course work includes several programming assignments and a "design+implement" final project. Prerequisite: experience in C/C++ and/or Java.See https://ccrma.stanford.edu/courses/256a/
Terms: Aut | Units: 3-4 | Grading: Letter (ABCD/NP)

CS 476B: Music, Computing, Design II: Virtual and Augmented Reality for Music (MUSIC 256B)

Aesthetics, design, and exploration of creative musical applications of virtual reality (VR) and augmented reality (AR), centered around VR and mobile technologies. Comparison between AR, VR, and traditional software design paradigms for music. Topics include embodiment, interaction design, novel instruments, social experience, software design + prototyping. Prerequisite: MUSIC 256A / CS 476A.
Terms: not given this year, last offered Winter 2016 | Units: 3-4 | Grading: Letter (ABCD/NP)
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