Eikosstatistics & data science

Machine Learning & Artificial Intelligence · Machine Learning & Ensembles

Machine Learning · Supervised Classification and Probabilistic Models

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Introduction

What this guide covers

Introduction Supervised Classification · Probabilistic Models

Overview

Topics covered

  • Introduction
  • Supervised Classification and Probabilistic Models
  • Bayesian Decision Theory
  • 1.1 Expected Risk Minimization and Loss Function
  • 1.2 The Asymmetric Loss Scenario
  • Discriminative Models vs. Generatives

Related concepts

Main connections

  • Linear algebra & matrices
  • Supervised machine learning
  • Regularization (Lasso / Ridge / Elastic Net)
  • Maximum likelihood estimation
  • Bayes' theorem & priors
  • Logistic regression

Compatible notation

Mathematical sample in LaTeX

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

P(Y=Ck∣𝐱)=p(𝐱∣Y=Ck)P(Y=Ck)p(𝐱)=p(𝐱∣Y=Ck)πk∑j=1Kp(𝐱∣Y=Cj)πj
View LaTeX sourceP(Y = C_k \mid \mathbf{x}) = \frac{p(\mathbf{x} \mid Y = C_k) P(Y = C_k)}{p(\mathbf{x})} = \frac{p(\mathbf{x} \mid Y = C_k) \pi_k}{\sum_{j=1}^K p(\mathbf{x} \mid Y = C_j) \pi_j}