Engineering Of Computer Applications
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About the Introduction to Computer Science Series at Stanford, The Philosophy, Why take CS106B?, Logistics of the Course, Introducing C++
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The History of Computing, Computer Science vs Programming, What Does the Computer Understand?, The Compilation Process, Java is an Object Oriented Language, Inheritance, Instance of a Class, The acm.program Hierarchy, Your First Java Program, A ConsoleProgram Example, The Graphics Window, The Sending-Messages-to-a-GLabel Example
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Applications of Reinforcement Learning, Markov Decision Process (MDP), Defining Value & Policy Functions, Value Function, Optimal Value Function, Value Iteration, Policy Iteration
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The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning
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Kernels, Mercer's Theorem, Non-linear Decision Boundaries and Soft Margin SVM, Coordinate Ascent Algorithm, The Sequential Minimization Optimization (SMO) Algorithm, Applications of SVM
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Topics: Welcome to CS106A, Course Staff, Why is the class called Programming Methodology?, Are you in the right class?, Class Logistics, Assignments and Grading, Extensions, Midterm and Final, Grade Breakdown, The Honor Code, Why Karel?
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Latent Semantic Indexing (LSI), Singular Value Decomposition (SVD) Implementation, Independent Component Analysis (ICA), The Application of ICA, Cumulative Distribution Function (CDF), ICA Algorithm, The Applications of ICA
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This course 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...more
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The Factor Analysis Model,0 EM for Factor Analysis, Principal Component Analysis (PCA), PCA as a Dimensionality Reduction Algorithm, Applications of PCA, Face Recognition by Using PCA
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Multinomial Event Model, Non-linear Classifiers, Neural Network, Applications of Neural Network, Intuitions about Support Vector Machine (SVM), Notation for SVM, Functional and Geometric Margins
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Partially Observable MDPs (POMDPs), Policy Search, Reinforce Algorithm, Pegasus Algorithm, Pegasus Policy Search, Applications of Reinforcement Learning
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April 25, 2008 lecture by Leah Buechley for the Stanford University Human Computer Interaction Seminar (CS547). Computational textile researchers weave, solder and sew electronics into cloth to build soft, flexible and wearable computers. Computational textiles or "e-textiles" is a young discipline, and developments in the field have so far been relegated almost...more
