Quick Start Guide

This guide will help you get started with MLAI quickly. We’ll cover the basic concepts and show you how to run your first examples.

Basic Usage

MLAI provides simple, educational implementations of machine learning algorithms. Here’s a basic example:

import mlai
import numpy as np
import matplotlib.pyplot as plt

# Generate some sample data
X = np.linspace(0, 10, 100).reshape(-1, 1)
y = np.sin(X).flatten() + 0.1 * np.random.randn(100)

# Create and fit a simple model
model = mlai.GaussianProcess(X, y)
model.fit()

# Make predictions
X_test = np.linspace(0, 10, 200).reshape(-1, 1)
y_pred, y_var = model.predict(X_test)

# Plot results
plt.figure(figsize=(10, 6))
plt.scatter(X, y, alpha=0.5, label='Data')
plt.plot(X_test, y_pred, 'r-', label='Prediction')
plt.fill_between(X_test.flatten(),
                y_pred - 2*np.sqrt(y_var),
                y_pred + 2*np.sqrt(y_var),
                alpha=0.3, label='95% Confidence')
plt.legend()
plt.show()

Tutorials

MLAI includes several tutorials to help you learn machine learning concepts:

Plotting Utilities

MLAI provides convenient plotting utilities for machine learning visualizations:

import mlai.plot as ma_plot

# Use MLAI's plotting utilities
ma_plot.set_defaults()  # Set default plotting parameters

# Create publication-quality plots
fig, ax = ma_plot.new_xy_figure()
# ... your plotting code here

Key Concepts

MLAI is designed with these principles in mind:

  1. Clarity: Code is written to be easily understood

  2. Mathematical Transparency: Mathematical concepts are explicit in the code

  3. Educational Focus: Every function serves a pedagogical purpose

  4. Reproducibility: Examples can be run end-to-end

Next Steps