ECE/CS/ME 539 Lecture Notes (power point files)
Last Modified: Monday, September 13, 2004
Lecture 1 Overview of the course
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Lecture 2 Applications of Artificial Neural Network
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Lecture 3 Basic Definitions of ANN
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Lecture 4 Learning (1)
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Lecture 5 Learning (2)
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Lecture 6 Learning (3)
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Lecture 7 Learning (4)
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Lecture 8 Learning (5)
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Lecture 9 Multilayer Perceptron (1): Model
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Lecture 10 Multilayer Perceptron (2): Learning
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Lecture 11 Multilayer Perceptron (3): Back Propagation
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Lecture 12 Multilayer Peceptron (4): Implementation
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Lecture 13 Multilayer Peceptron (5): Details
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Lecture 14 Multiplayer Peceptron (6): Model Selection
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Lecture 15 Classification (1)
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Lecture 16 Classification (2)
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Lecture 17 Support Vector Machine (1): Basics
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Lecture 18 Support Vector Machine (2): Nonseparable case
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Lecutre 19 Support Vector Machine (3): Kernel
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Lecture 20 clustering (1): Kmeans algorithm
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Lecture 21 clustering (2): distortion measures
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Lecture 22 clustering (3): Self-Organization Map
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Lecture 23 clustering (4): prob density function estimation
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Lecture 24 Radial basis function (1
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Lecture 25 Radial basis function (2)
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Lecture 26 Model (1): time series analysis
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Lecture 27 Model (2): system identification and control
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Lecture 28 Model (3): expert system
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Lecture 29 Fuzzy Systems (1)
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Lecture 30 Fuzzy Systems (2)
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Lecture 31 Fuzzy Systems (3)
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Lecture 32 Fuzzy Systems (4)
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Lecture 33 Fuzzy Logic Control (1)
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Lecture 34 Fuzzy Logic Control (2)
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Lecture 35 Fuzzy Logic Control (3)
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Lecture 36 Genetic Algorithm (1)
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Lecture 37 Genetic Algorithm (2)
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Lecture 38 Mixture of Expert Neural Network
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Lecture 39 Hopfield Network
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