Tutorials

This section contains tutorials and examples that demonstrate how to use MLAI for learning machine learning concepts.

Tutorials:

• Linear Regression Tutorial
  • Mathematical Background
  • Implementation in MLAI
  • Creating the Model
  • Training the Model
  • Visualizing the Results
  • Model Evaluation
  • Regularization
  • Comparing Different Basis Functions
  • Bayesian Linear Regression
  • Key Concepts
  • Advantages and Limitations
  • Further Reading
• Logistic Regression Tutorial
  • Mathematical Background
  • Log-Odds and Link Function
  • Implementation in MLAI
  • Creating the Model
  • Training the Model
  • Visualizing the Results
  • Model Evaluation
  • Gradient Descent Training
  • Comparing Different Basis Functions
  • Key Concepts
  • Advantages and Limitations
  • Further Reading
• Perceptron Algorithm Tutorial
  • Mathematical Background
  • Implementation in MLAI
  • Training the Perceptron
  • Visualizing the Results
  • Key Concepts
  • Limitations
  • Extensions
  • Further Reading
• Basis Functions Tutorial
  • Mathematical Background
  • Types of Basis Functions in MLAI
  • Using Basis Functions with Linear Models
  • Comparing Different Basis Functions
  • Key Concepts
  • Choosing the Right Basis Function
  • Limitations and Considerations
  • Further Reading
• Gaussian Process Tutorial
  • ax_default()
  • meanplot()
  • gpplot()
• Deep Gaussian Process Tutorial
• Mountain Car Reinforcement Learning

Getting Started

If you’re new to MLAI, we recommend starting with the Quick Start Guide guide before diving into these tutorials.

Each tutorial is designed to be educational and includes:

• Clear explanations of the underlying concepts

• Step-by-step code examples

• Mathematical background where relevant

• Visualizations to aid understanding

Tutorial Structure

• Linear Regression: Foundation of supervised learning with basis functions

• Logistic Regression: Binary classification with probabilistic modeling

• Perceptron Algorithm: Simple linear classifier and learning algorithm

• Basis Functions: Feature transformation for non-linear modeling

• Gaussian Process Tutorial: Introduction to Gaussian Processes

• Deep GP Tutorial: Advanced concepts with Deep Gaussian Processes

• Mountain Car Example: Reinforcement learning with the mountain car environment