Labs

The current version of the labs for ISLP are included here.

Package versions

Attention

Python packages change frequently. The labs here are built with ISLP_labs/v2.2. Visit the lab git repo for specific instructions to install the frozen environment.

A zip file containig all the labs and data files can be downloaded here ISLP_labs/v2.2.zip.

Warning

The version of the ISLP library used to build these labs may differ slightly from the one documented here. The labs are built with ISLP/v0.4.0.

The Binder link above will run ISLP_labs/v2.2 with library version ISLP/v0.4.0.

• Introduction to Python
  • Getting Started
  • Basic Commands
  • Introduction to Numerical Python
  • Graphics
  • Sequences and Slice Notation
  • Indexing Data
    • Indexing Rows, Columns, and Submatrices
    • Boolean Indexing
  • Loading Data
    • Reading in a Data Set
    • Basics of Selecting Rows and Columns
    • More on Selecting Rows and Columns
  • For Loops
    • String Formatting
  • Additional Graphical and Numerical Summaries
• Linear Regression
  • Importing packages
    • New imports
    • Inspecting Objects and Namespaces
  • Simple Linear Regression
    • Using Transformations: Fit and Transform
    • Defining Functions
  • Multiple Linear Regression
  • Multivariate Goodness of Fit
    • List Comprehension
  • Interaction Terms
  • Non-linear Transformations of the Predictors
  • Qualitative Predictors
• Logistic Regression, LDA, QDA, and KNN
  • The Stock Market Data
  • Logistic Regression
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Naive Bayes
  • K-Nearest Neighbors
    • Tuning Parameters
    • Comparison to Logistic Regression
  • Linear and Poisson Regression on the Bikeshare Data
    • Linear Regression
    • Poisson Regression
• Cross-Validation and the Bootstrap
  • The Validation Set Approach
  • Cross-Validation
  • The Bootstrap
    • Estimating the Accuracy of a Statistic of Interest
    • Estimating the Accuracy of a Linear Regression Model
• Linear Models and Regularization Methods
  • Subset Selection Methods
    • Forward Selection
    • Choosing Among Models Using the Validation Set Approach and Cross-Validation
    • Best Subset Selection
  • Ridge Regression and the Lasso
    • Ridge Regression
    • Estimating Test Error of Ridge Regression
    • Fast Cross-Validation for Solution Paths
    • Evaluating Test Error of Cross-Validated Ridge
    • The Lasso
  • PCR and PLS Regression
    • Principal Components Regression
    • Partial Least Squares
• Non-Linear Modeling
  • Polynomial Regression and Step Functions
  • Splines
  • Smoothing Splines and GAMs
    • Additive Models with Several Terms
    • ANOVA Tests for Additive Models
  • Local Regression
• Tree-Based Methods
  • Fitting Classification Trees
  • Fitting Regression Trees
  • Bagging and Random Forests
  • Boosting
  • Bayesian Additive Regression Trees
• Support Vector Machines
  • Support Vector Classifier
  • Support Vector Machine
  • ROC Curves
  • SVM with Multiple Classes
  • Application to Gene Expression Data
• Deep Learning
  • Torch-Specific Imports
  • Single Layer Network on Hitters Data
    • Linear Models
    • Specifying a Network: Classes and Inheritance
    • Cleanup
  • Multilayer Network on the MNIST Digit Data
  • Convolutional Neural Networks
    • Hardware Acceleration
  • Using Pretrained CNN Models
  • IMDB Document Classification
    • Comparison to Lasso
  • Recurrent Neural Networks
    • Sequential Models for Document Classification
    • Time Series Prediction
• Survival Analysis
  • Brain Cancer Data
  • Publication Data
  • Call Center Data
• Unsupervised Learning
  • Principal Components Analysis
  • Matrix Completion
  • Clustering
    • $K$-Means Clustering
    • Hierarchical Clustering
  • NCI60 Data Example
    • PCA on the NCI60 Data
    • Clustering the Observations of the NCI60 Data
• Multiple Testing
  • Review of Hypothesis Tests
  • Family-Wise Error Rate
  • False Discovery Rate
  • A Re-Sampling Approach