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From Virginia Bordignon
Lecture 13: Neural networks, Part 2 -
From Virginia Bordignon
Lecture 13: Neural networks, part 1 -
From Virginia Bordignon
Lecture 12, Part 2: Generalization theory -
From Virginia Bordignon
Lecture 12, Part 1: Kernel methods -
From Virginia Bordignon
Lecture 11: SVM, Part 3 -
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Lecture 11: Perceptron, Part 2 -
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Lecture 11: Logistic Regression, Part 1 -
From Virginia Bordignon
Lecture 10: PCA -
From Virginia Bordignon
Lecture 10: Linear discriminant analysis -
From Virginia Bordignon
Lecture 10: Naive Bayes classifier -
From Virginia Bordignon
Lecture 9: Nearest neighbor rules, Part 2 -
From Virginia Bordignon
Lecture 9: L1 Regularization, Part 1 -
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Lecture 8.2: Regularization -
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Lecture 8: Least Squares -
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Lecture 7: Maximum Likelihood
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