Statistical Learning

Tracks

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1 8 Years Later (Second Edition of the Course) 8 Years Later (Second Edition of the Course)
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2 Introduction Introduction
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3 The Supervised Learning Problem The Supervised Learning Problem
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4 What is Statistical Learning? What is Statistical Learning?
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5 1 Opening Remarks 1 Opening Remarks
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6 Philosophy Philosophy
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7 Simple linear regression using a single predictor X. Simple linear regression using a single predictor X.
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8 Assessing the Accuracy of the Coefficient Estimates Assessing the Accuracy of the Coefficient Estimates
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9 Notation Notation
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10 4 Classification 4 Classification
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11 Unsupervised learning Unsupervised learning
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12 What is f(X) good for? What is f(X) good for?
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13 The regression function f(x) The regression function f(x)
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14 How to estimate f How to estimate f
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15 3 Model Selection and Bias Variance Tradeoff 3 Model Selection and Bias Variance Tradeoff
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16 3 Multiple Linear Regression 3 Multiple Linear Regression
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17 2 Hypothesis Testing and Confidence Intervals 2 Hypothesis Testing and Confidence Intervals
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18 Linear regression for the advertising data Linear regression for the advertising data
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19 Estimation of the parameters by least squares be the prediction for Y based on the ith Estimation of the parameters by least squares be the prediction for Y based on the ith
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20 5 Discriminant Analysis 5 Discriminant Analysis
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21 2 Dimensionality and Structured Models 2 Dimensionality and Structured Models
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22 Bagging continued Bagging continued
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23 Py.3 Graphics I 2023 Py.3 Graphics I 2023
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24 The Netflix prize The Netflix prize
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25 Statistical Learning versus Machine Learning Statistical Learning versus Machine Learning
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26 Confidence intervals continued Confidence intervals continued
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27 9 Quadratic Discriminant Analysis and Naive Bayes 9 Quadratic Discriminant Analysis and Naive Bayes
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28 Some important questions Some important questions
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29 Is at least one predictor useful? Is at least one predictor useful?
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30 Deciding on the important variables Deciding on the important variables
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31 Classification Classification
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32 Py Logistic Regression I 2023 Py Logistic Regression I 2023
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33 Py Linear Discriminant Analysis (LDA) I 2023 Py Linear Discriminant Analysis (LDA) I 2023
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34 4 Logistic Regression Case Control Sampling and Multiclass 4 Logistic Regression Case Control Sampling and Multiclass
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35 Results for ethnicity Results for ethnicity
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36 6 Gaussian Discriminant Analysis (One Variable) 6 Gaussian Discriminant Analysis (One Variable)
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37 Varying the threshold Varying the threshold
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38 5 More on the Bootstrap 5 More on the Bootstrap
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39 Boosting algorithm for regression trees Boosting algorithm for regression trees
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40 Radial Kernel Radial Kernel
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41 Py Support Vector Machines I 2023 Py Support Vector Machines I 2023
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42 Hyperplane Definition Hyperplane Definition
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43 Separating Hyperplane Separating Hyperplane
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44 Maximum Margin Classifier Maximum Margin Classifier
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45 2.Support Vector Classifier 2.Support Vector Classifier
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46 Py Setting Up Python I 2023 Py Setting Up Python I 2023
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47 1 Polynomials and Step Functions 1 Polynomials and Step Functions
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48 7 The Lasso 7 The Lasso
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49 Local Regression Local Regression
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50 Py Data Types, Arrays, and Basics I 2023 Py Data Types, Arrays, and Basics I 2023
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