analysis of linear systems
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Complexity; log, linear, quadratic, exponential algorithms
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This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace's equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications.
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Note: This course is being offered by Stanford this summer as an online course for credit. It can be taken individually, or as part of a master’s degree or graduate certificate earned online through the Stanford Center for Professional Development. This course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming...more
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This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. It also looks at case studies of working systems and readings from the curre...more
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The discussion of Gadamer and Hirsch continues in this lecture, which further examines the relationship between reading and interpretation. Through a comparative analysis of these theorists, Professor Paul Fry explores the difference between meaning and significance, the relationship between understanding and paraphrasing, and the nature of the gap between the reader and the text. Through Wolfgang Iser's essay, "The Reading Process," the n...more
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Conscious of the Present; Conscious of the Past: Language (cont.); Vision and Memory
Yale / Psychology
This lecture finishes the discussion of language by briefly reviewing two additional topics: communication systems in non-human primates and other animals, and the relationship between language and thought. The majority of this lecture is then spent on introducing students to major theories and discoveries in the fields of perception, attention and memory. Topics include why we see certain visual illusions, why we don't always see everythi...more
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November 30, 2007 lecture by Ted Selker for the Stanford University Human-Computer Interaction Seminar (CS 547). This talk demonstrates that Artificial intelligence can competently improve human interaction with systems and even each other in a myriad of natural scenarios.
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Control - Overview, Joint Space Control, Resolved Motion Rate Control, Natural Systems, Dissipative Systems, Example, Passive System Stability
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Concentrates on recognizing and solving convex optimization problems that arise in engineering. Topics include: Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal proc...more
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Moore uses the core/context analysis framework to discuss how to overcome inertia in strategy and structure.
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Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language.
