Machine Learning: Foundations and Decision Theory — Advanced Reference

Introduction

What this guide covers

Introduction Introduction # Statistical Decision Theory

Overview

Topics covered

• Introduction
• Machine Learning Fundamentals and Decision Theory
• 1. Mathematical Formalization of Supervised Learning
• 2. Bayesian Decision Theory
• 2.1 Derivation of the Bayes Predictor for Common Losses
• Case 1: Quadratic Loss ( Loss) in Regression

Related concepts

Main connections

• Regularization (Lasso / Ridge / Elastic Net)
• Variance & standard deviation
• Supervised machine learning
• Overfitting & cross-validation
• Bayes' theorem & priors
• Bootstrap resampling

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.