Introduction to Machine Learning, Linear Regression-Introduction
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Introduction
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Course Textbooks
Introduction to Machine Learning, Linear Regression-Programming Assignments
Things of Note for Programming Assignments
Introduction to Machine Learning, Linear Regression-Peer Review and Honor Code Expectations
Peer Review Guidelines and Expectations
A Note About Peer Review Resubmissions
Honor Code Expectations
Introduction to Machine Learning, Linear Regression-Linear Regression Model Introduction
ISLR 3.1: Simple Linear Regression
Simple Linear Regression
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Introduction to Machine Learning, Linear Regression-Linear Regression Model Optimization
ISLR 3.1.1: Estimating the Coefficients
Least Squared Method
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Introduction to Machine Learning, Linear Regression-Linear Regression Model Evaluation
ISLR 3.1.2: Assessing the Accuracy of the Coefficient Estimates
ISLR 3.1.3: Assessing the Accuracy of the Model
Model Fitness and R-squared
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Coefficient Significance and Test Error
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Introduction to Machine Learning, Linear Regression-Slide Decks
Module 1 Slides
Multilinear Regression-Introduction to Multilinear Regression: Polynomial Regression Type
ISLR 3.2: Multiple Linear Regression
ISLR 3.3.2: Extensions of the Linear Model
Linear Regression with Higher-Order Terms: Polynomial Regression
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Multilinear Regression-Bias-Variance Trade-Off
ISLR 2.2.2: The Bias-Variance Trade-Off
Bias-Variance Trade-Off
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Multilinear Regression-Multiple Linear Regression Model Optimization
ISLR 3.3.3: Potential Problems
Linear Regression with Multiple Features
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Feature Selection, Correlation, and Interaction
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Multilinear Regression-Slide Decks
Module 2 Slides
Logistic Regression-Introduction to Logistic Regression
ISLR 4.1 - 4.3.1: An Overview of Classification - Logistic Regression
Logistic Regression Introduction
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Logistic Regression-Logistic Regression Model Optimization
ISLR 4.3.2: Estimating the Regression Coefficients
Logistic Regression Optimization
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Logistic Regression-Evaluating Logistic Regression Models
Confusion Matrix
ISLR 6.2.1- 6.2.3 and 5.1: Ridge Regression and Cross-Validation
Performance Metrics in Classification
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Logistic Regression-Coding Examples: Sklearn Library
Sklearn Library Usage and Examples
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Logistic Regression
Logistic Regression-Slide Decks
Module 3 Slides
Non-parametric Models-Introduction to KNN Model
ISLR: K-Nearest Neighbors
Intro to Non-parametric and K-nearest Neighbors
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Non-parametric Models-Introduction to Decision Tree Model
ISLR 8.1.1: The Basics of Decision Trees-Regression Trees
Decision Tree Intro, Decision Tree Regressor
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Non-parametric Models-Decision Tree Metrics
ISLR 8.1.2: Classification Trees
Decision Tree Classifier, Metrics (Gini and Entropy)
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Decision Tree Classifier
Non-parametric Models-Optimizing Decision Tree Models
Sklearn Usage, DT Hyperparameters and Early Stopping
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ISLR: Tree Pruning
Minimal Cost-complexity Pruning
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Non-parametric Models-Slide Decks
Module 4 Slides
Ensemble Methods-Introduction to Ensembles: Bagging
ISLR 8.2.1, 8.2.2: Bagging and Random Forests
Ensemble Method Intro: Random Forest
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Ensemble Methods-Introduction to Boosting Ensembles
ISLR 8.2.3: Boosting
Boosting Introduction
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Ensemble Methods-Boosting Algorithms
ESLII 10.1 - 10.4: Boosting Methods - Exponential Loss and AdaBoost
AdaBoost Algorithm
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ESLII 10.10, 10.11: Gradient Boosting
Gradient Boosting
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Ensemble Methods-Slide Decks
Module 5 Slides
Kernel Method-Introduction to Support Vector Machine Model
ISLR 9.1: Maximal Margin Classifier
Support Vector Machine Introduction
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Kernel Method-Optimizing SVM
ISLR 9.2: Support Vector Classifiers
Support Vector Machine: Soft Margin Classifier
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ISLR 9.3: Support Vector Machines
Support Vector Machine: Kernel Trick
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Kernel Method-SVM Performance
Support Vector Machine: Performance
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Kernel Method-Slide Decks
Module 6 Slides