MLAI: Machine Learning and Adaptive Intelligence Teaching Library

Welcome to the MLAI documentation! This package provides simple models, tutorials, and plotting routines designed for teaching and lecturing on machine learning fundamentals.

Contents:

• Installation Guide
  • Prerequisites
  • Installation with Poetry
  • Installation with pip
  • Optional Dependencies
  • Verifying Installation
  • Next Steps
• Quick Start Guide
  • Basic Usage
  • Tutorials
  • Plotting Utilities
  • Key Concepts
  • Next Steps
• Tutorials
  • Linear Regression Tutorial
  • Logistic Regression Tutorial
  • Perceptron Algorithm Tutorial
  • Basis Functions Tutorial
  • Gaussian Process Tutorial
  • Deep Gaussian Process Tutorial
  • Mountain Car Reinforcement Learning
  • Getting Started
  • Tutorial Structure
• API Reference
  • MLAI Core Module
  • Plotting Module
  • Gaussian Process Tutorial Module
  • Deep Gaussian Process Tutorial Module
  • Mountain Car Module
  • Module Overview
  • Code Style
• Contributing to MLAI
  • Project Philosophy
  • Getting Started
  • Development Workflow
  • Coding Standards
  • Testing
  • Documentation
  • Submitting Changes
  • Code of Conduct
  • Questions?
• Project Tenets
  • Core Principles
  • Applying the Tenets
  • For Contributors

Project Information:

• Code Improvement Proposals (CIPs)
• Project Backlog

Indices and tables

• Index

• Module Index

• Search Page