Eikosstatistics & data science

Machine Learning & Artificial Intelligence · Data Mining & Feature Engineering

Data Mining: Supervised Learning — Fundamentals of Classification and Regression

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Doctoral Level Technical Treatise

Overview

Topics covered

  • Introduction
  • Supervised Learning: Classification and Regression
  • Bayesian Decision Theory and Risk Minimization
  • 1.1 The Bias-Variance Decomposition
  • Multiple Linear Regression: Matrix Formulation, Assumptions and Inference
  • 2.1 Gauss-Markov assumptions

Related concepts

Main connections

  • Regularization (Lasso / Ridge / Elastic Net)
  • Supervised machine learning
  • Linear algebra & matrices
  • Overfitting & cross-validation
  • Variance & standard deviation
  • Cross-validation & train/test splits

Compatible notation

Mathematical sample in LaTeX

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

R(f)=𝔼(X,Y)∼ℙ[L(Y,f(X))]=∫𝒳×𝒴L(y,f(x))dℙ(x,y)
View LaTeX sourceR(f) = \mathbb{E}_{(X,Y) \sim \mathbb{P}}[L(Y, f(X))] = \int_{\mathcal{X} \times \mathcal{Y}} L(y, f(x)) \, d\mathbb{P}(x, y)