applications of machine learning
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Half a million people in the UK are on the autistic spectrum, all finding it hard to make sense of the world around them. The disorder impacts both the highly intelligent (some employed in the City) and the profoundly learning disabled (like my late son).
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U01_L2_T1_we1 Adding Whole Numbers and Applications 1.
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U01_L2_T1_we2 Adding Whole Numbers and Applications 2.
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U01_L2_T1_we3 Adding Whole Numbers and Applications 3.
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U01_L2_T1_we4 Adding Whole Numbers and Applications 4.
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Analysis and optimized design of monolithic operational amplifiers and wide-band amplifiers; methods of achieving wide-band amplification, gain-bandwidth considerations; analysis of noise in integrated circuits and low noise design. Precision passive elements, analog switches, amplifiers and comparators, voltage reference in NMOS and CMOS circuits, Serial, successive-approximation, and parallel analog-to-digital converters. Switched-...more
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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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An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations
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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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This course uses the theory and application of atomistic computer simulations to model, understand, and predict the properties of real materials. Specific topics include: energy models from classical potentials to first-principles approaches; density functional theory and the total-energy pseudopotential method; errors and accuracy of quantitative predictions: thermodynamic ensembles, Monte Carlo sampling and molecular dynamics simulations...more
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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 -- allowi...more
