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Published byClifton Harris Modified over 9 years ago
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Summary of This Course Huanhuan Chen
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Outline Basics about Signal & Systems Bayesian inference PCVM Hidden Markov Model Kalman filter Extended Kalman filter Particle filter Bayesian Networks Nonlinear Dynamic Systems Echo State Networks
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Signal & System Time Domain Signal properties Frequency Domain Fourier transform Z-transform Laplace transform System State space model
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Machine learning & Bayesians
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Sparse Bayesian Learning What’s Bayesian? Why Bayesian? How to use Bayesian?
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Kalman filtering : Recursive update of state Kalman filtering algorithm: repeat… Time update: from X t|t, compute a priori distrubution X t+1|t Measurement update: from X t+1|t (and given y t+1 ), compute a posteriori distribution X t+1|t+1 X0X0 X1X1 X2X2 X3X3 X4X4 X5X5 Y1Y1 Y2Y2 Y3Y3 Y4Y4 Y5Y5 …
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Particle filter 7
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Bayesian Networks Inference Structure Learning
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Echo State Networks
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