Statistical Learning with R

Stanford University via YouTube

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Learn supervised and unsupervised machine learning methods through this comprehensive introductory course that emphasizes practical implementation over mathematical theory. Master essential statistical learning techniques including linear and polynomial regression, logistic regression, and linear discriminant analysis, then advance to sophisticated methods like cross-validation, bootstrap sampling, and regularization techniques such as ridge and lasso regression. Explore nonlinear modeling approaches including splines and generalized additive models, tree-based methods, random forests, and boosting algorithms. Delve into support vector machines, neural networks, deep learning architectures including convolutional and recurrent networks, and survival analysis models. Gain proficiency in unsupervised learning through principal component analysis and clustering methods including k-means and hierarchical clustering. Develop practical R programming skills through dedicated tutorials that progress from basic syntax to advanced implementation of each statistical technique. Study multiple testing procedures and their applications in controlling family-wise error rates and false discovery rates. Work with real-world datasets and case studies that demonstrate the application of these methods across various domains including brain cancer data, publication data, and call center analytics. Access comprehensive coverage of material from "An Introduction to Statistical Learning, with Applications in R" while building hands-on experience through extensive R coding sessions that implement every major concept and algorithm presented in the theoretical lectures.

Syllabus

Statistical Learning: 1.1 Opening Remarks
Statistical Learning: 8 Years Later (Second Edition of the Course)
Statistical Learning: 1.2 Examples and Framework
Statistical Learning: 2.1 Introduction to Regression Models
Statistical Learning: 2.2 Dimensionality and Structured Models
Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
Statistical Learning: 2.4 Classification
Statistical Learning: 2.R Introduction to R
Statistical Learning: 3.1 Simple linear regression
Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
Statistical Learning: 3.3 Multiple Linear Regression
Statistical Learning: 3.4 Some important questions
Statistical Learning: 3.5 Extensions of the Linear Model
Statistical Learning: 3.R Regression in R
Statistical Learning: 4.1 Introduction to Classification Problems
Statistical Learning: 4.2 Logistic Regression
Statistical Learning: 4.3 Multivariate Logistic Regression
Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass
Statistical Learning: 4.5 Discriminant Analysis
Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)
Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)
Statistical Learning: 4.8 Generalized Linear Models
Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
Statistical Learning: 4.R.1 Logistic Regression
Statistical Learning: 4.R.2 Linear Discriminant Analysis
Statistical Learning: 4.R.3 Nearest Neighbor Classification
Statistical Learning: 5.1 Cross Validation
Statistical Learning: 5.2 K-fold Cross Validation
Statistical Learning: 5.3 Cross Validation the wrong and right way
Statistical Learning: 5.4 The Bootstrap
Statistical Learning: 5.5 More on the Bootstrap
Statistical Learning: 5.R.1 Cross Validation
Statistical Learning: 5.R.2 Bootstrap
Statistical Learning: 6.1 Introduction and Best Subset Selection
Statistical Learning: 6.2 Stepwise Selection
Statistical Learning: 6.3 Backward stepwise selection
Statistical Learning: 6.4 Estimating test error
Statistical Learning: 6.5 Validation and cross validation
Statistical Learning: 6.6 Shrinkage methods and ridge regression
Statistical Learning: 6.7 The Lasso
Statistical Learning: 6.8 Tuning parameter selection
Statistical Learning: 6.9 Dimension Reduction Methods
Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares
Statistical Learning: 6.R.1 Markdown in RStudio and Best Subset Regression
Statistical Learning: 6.R.2 Forward Stepwise Regression
Statistical Learning: 6.R.3 Model Selection and Cross-Validation
Statistical Learning: 6.R.4 Ridge Regression and Lasso
Statistical Learning: 7.1 Polynomials and Step Functions
Statistical Learning: 7.2 Piecewise Polynomials and Splines
Statistical Learning: 7.3 Smoothing Splines
Statistical Learning: 7.4 Generalized Additive Models and Local Regression
Statistical Learning: 7.R.1 Polynomials in GLMs
Statistical Learning: 7.R.2 Splines and GAMs
Statistical Learning: 8.1 Tree based methods
Statistical Learning: 8.2 More details on Trees
Statistical Learning: 8.3 Classification Trees
Statistical Learning: 8.4 Bagging
Statistical Learning: 8.5 Boosting
Statistical Learning: 8.6 Bayesian Additive Regression Trees
Statistical Learning: 8.R.1 Fitting Trees
Statistical Learning: 8.R.2 Random Forests and Boosting
Statistical Learning: 9.1 Optimal Separating Hyperplane
Statistical Learning: 9.2.Support Vector Classifier
Statistical Learning: 9.3 Feature Expansion and the SVM
Statistical Learning: 9.4 Example and Comparison with Logistic Regression
Statistical Learning: 9.R.1 Support Vector Classifier
Statistical Learning: 9.R.2 Nonlinear Support Vector Machine
Statistical Learning: 10.1 Introduction to Neural Networks
Statistical Learning: 10.2 Convolutional Neural Networks
Statistical Learning: 10.3 Document Classification
Statistical Learning: 10.4 Recurrent Neural Networks
Statistical Learning: 10.5 Time Series Forecasting
Statistical Learning: 10.6 Fitting Neural Networks
Statistical Learning: 10.7 Interpolation and Double Descent
Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data
Statistical Learning: 10.R.2 Convolutional Neural Networks in R
Statistical Learning: 10.R.3 Document Classification
Statistical Learning: 10.R.4 Recurrent Neural Networks
Statistical Learning: 11.1 Introduction to Survival Data and Censoring
Statistical Learning: 11.2 Proportional Hazards Model
Statistical Learning: 11.3 Estimation of Cox Model with Examples
Statistical Learning: 11.4 Model Evaluation and Further Topics
Statistical Learning: 11.R.1 Survival Curves Brain Cancer Data
Statistical Learning: 11.R.2 Cox Models I Publication Data
Statistical Learning: 11.R.3 Cox Models II Call Center Data
Statistical Learning: 12.1 Principal Components
Statistical Learning: 12.2 Higher order principal components
Statistical Learning: 12.3 k means Clustering
Statistical Learning: 12.4 Hierarchical Clustering
Statistical Learning: 12.5 Matrix Completion
Statistical Learning: 12.6 Breast Cancer Example
Statistical Learning: 12.R.1 Principal Components
Statistical Learning: 12.R.2 K means Clustering
Statistical Learning: 12.R.3 Hierarchical Clustering
Statistical Learning: 13.1 Introduction to Hypothesis Testing
Statistical Learning: 13.1 Introduction to Hypothesis Testing II
Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
Statistical Learning: 13.3 Bonferroni Method for Controlling FWER
Statistical Learning: 13.4 Holm's Method for Controlling FWER
Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method
Statistical Learning: 13.6 Resampling Approaches
Statistical Learning: 13.6 Resampling Approaches II
Statistical Learning: 13.R.1 Bonferroni and Holm
Statistical Learning: 13.R.1 Bonferroni and Holm II