📖 An Introduction to Statistical Learning with Applications in Python (ISLP) Solutions

As a pure math student seeking an introduction into the foundations of machine learning, ISLP written by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, and Jonathan Taylor is regarded as one of the best entry points. Not only a quality textbook, but highly accessible! Click to read for free.

The text covers mathematical and statistical theory of machine learning as well as applied labs in the programming language Python.

Note: The text assumes a moderate level of mathematical maturity and features an earlier edition with labs written in the statistical language R.

📜 Original solutions to the exercises

As a reinforcement of concepts you will find exercise solutions below written in JupyterLab using Python and Markdown hosted on GitHub.

• Chapter 2: Statistical Learning
  • Topics: What Is Statistical Learning?, Assessing Model Accuracy
  • Applied
    • Exercise 9, Exercise 10
• Chapter 3: Linear Regression
  • Topics: Simple Linear Regression, Multiple Linear Regression, K-Nearest Neighbors
  • Conceptual
    • Solutions
  • Applied
    • Exercise 8, Exercise 9, Exercise 10, Exercise 11, Exercise 12, Exercise 13, Exercise 14, Exercise 15
• Chapter 4: Classification
  • Topics: An Overview, Why Not Linear Regression?, Logistic Regression, Generative Models for Classification (LDA, QDA, Naive Bayes), Generalized Linear Models (Linear Regression, Poisson Regression)
  • Conceptual
    • Solutions
  • Applied
    • Exercise 13, Exercise 14, Exercise 15, Exercise 16