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CS540 Introduction to Artificial IntelligenceLecture 14
Young WuBased on lecture slides by Jerry Zhu and Yingyu Liang
July 2, 2019
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PerceptronReview
Perceptron update rule.
Perceptron termination condition.
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Logistic RegressionReview
Logistic update rule.
Logistic cost function.
Convexity.
Hessian, Laplacian, eigenvalue.
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Neural NetworkReview
Activation.
Backpropogation.
L1 and L2 regularization.
Cross validation.
Multi class classification.
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Multi Layer Neural Network ExampleReview
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LTU Activation ExampleReview
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Support Vector MachineReview
Hard margin support vector.
Soft margin maximization.
Subgradient descent.
Kernel trick.
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Support Vector Margin ExampleReview
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Feature Vector to Kernel ExampleReview
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Decision TreeReview
Entropy.
Information gain.
Bagging and boosting.
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Decision Tree ExampleReview
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K Nearest NeighborReview
Distance functions.
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K Nearest Neighbor Cross Validation ExampleReview
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Convolutional Neural NetworkReview
Convolution.
Pooling.
Trained weights.
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Convolutional Weights Count ExampleReview
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Computer VisionReview
Histogram of Gradients Features.
Scale Invariant Feature Transform.
Block normalization.
Dominant orientation.
Harr Features.
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Histogram of Gradient ExampleReview
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Natural Language ProcessingReview
Bigram and trigram model.
Transition matrix.
Random word generation.
Bayes rule.
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Document Bayes Rule ExampleReview
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Bayesian NetworkReview
Conditional probability table.
Maximum likelihood estimation.
Training vs inference.
Chow Liu algorithm.
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Common Cause Network ExampleReview
