Programming Style
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(Generalized) Linear-Fractional Program, Quadratic Program (QP), Quadratically Constrained Quadratic Program (QCQP), Second-Order Cone Programming, Robust Linear Programming, Geometric Programming, Example (Design Of Cantilever Beam), GP Examples (Minimizing Spectral Radius Of Nonnegative Matrix)
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Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)
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May 16, 2008 lecture by Rob Miller for the Stanford University Human Computer Interaction Seminar (CS547). Rob Miller discusses some of the explorations into keyword programming in the web automation domain, and also in other domains such as Java development. One surprising result is that programming language syntax often has relatively little information content, and can be inferred automatically from only a handful of keywords --...more
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Binary Numbers, Programming Languages, Working in Linux, and Programming in C
Harvard / Computer Science
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November 16, 2007 lecture by Ge Wang for the Stanford University Human-Computer Interaction Seminar. In the first part of this talk, Ge presents the design, philosophy, and development of ChucK, a computer music programming language intending to provide a different approach, expressiveness, and thinking with respect to time and parallelism in audio programming - as well as a platform for precise and rapid experimentation. In the second...more
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Comments: Latex Typesetting Style, Recap: Primal Decomposition, Dual Decomposition, Dual Decomposition Algorithm, Finding Feasible Iterates, Interpretation, Decomposition With Constraints, Primal Decomposition (With Constraints) Algorithm, Example (Primal Decomposition With Constraints), Dual Decomposition (With Constraints), Dual Decomposition (With Constraints) Algorithm, General Decomposition Structures, General Form, Primal...more
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Programming Methodology is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widely-used Java programming language along with good software...more
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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++...more
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Topics include: Advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms; the functional paradigm (using LISP) and concurrent programming (using C and C++); brief survey of other modern languages such as Python, Objective C, and C#. Prerequisites: Programming and problem solving at the Programming Abstractions level. Prospective students should know a reasonable amount of C++....more
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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...more
