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Machine Learning Class (Winter 2019-2020)

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Lecture 4.1: Classification | k-NN | ML19

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Lecture 4.2: Cross-Validation | Decision Trees | ML19

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Lecture 5.1: Decision Trees (cont.) | ML19

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Lecture 5.2: Random Forest | ML19

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Lecture 6.1: Bayes Theorem | Statistical Decision Theory | ML19

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Lecture 6.2: Statistical Decision Theory (cont.) | Multivariate Normal | QDA | ML19

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Lecture 7.1: Linear Regression | ML19

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Lecture 7.2: Linear Regression (cont.) | ML19

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Lecture 8.1: Regularized Linear Regression | Ridge | ML19

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Lecture 8.2: Regularized Linear Regression | Lasso | ML19

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Lecture 9.1: Gaussian Process Regression | ML19

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Lecture 9.2: Gaussian Process Regression (cont.) | ML19

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Lecture 11.1: Perceptron | ML19

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Lecture 11.2: Perceptron (cont.) | Multi-Layer Perceptron | ML19

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Lecture 12.2: Projection Trick | Function Counting Theorem | ML19

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