nhat-le01 / machine_learning

Summary of Machine Learning: A Probabilistic Perspective book

Introduction

This book is about machine learning algorithms. It follows strictly to the content of the textbook: "Machine Learning: A probabilistic perspective" by Kevin P. Murphy

The aim of this book is to summarize the main points of each chapter. It would be better to read each chapter in that book first and refer to this book as a summarization.

Note

• You can refer the TOC on the left hand side to navigate across the book.
• All the images and equations I will extract directly from the textbook (I will refer figure number directly). All the images' credit goes to the author.

Suggested reading chapters by order

• Part 1: Introduction:

  • Chapter 1, 2: Introduction
  • Chapter 3, 4: Generative Classifier or Regressor
  • Chapter 7, 8: Discriminative Classifier or Regressor

• Part 2: Fundamental algorithms:

  • Chapter 11: Mixture models and EM algorithm
  • Chapter 12: Dimension Reduction
  • Chapter 14: Kernel methods and SVM
  • Chapter 15: Gaussian Proceeses
  • Chapter 16: Decision Tree, Boosting
  • Chapter 25: Clustering

• Part 3: Graphical Models

  • Chapter 10, 17: Directed Graphical Models + HMM
  • Chapter 19: Undirected Graphical Models + CRF
  • Chapter 20: Exact Inference in Graphical Models
  • Chapter 21, 22: Approximate Inference: Variational Inference
  • Chapter 23, 24: Approximate Inference: Monte Carlo Inference