Machine Learning for Networking¶

Table of Contents

• Chapter 1: Introduction
  • How Networks Run
  • The Role of Machine Learning
  • Why Now?
  • What You Will Learn in This Book
• Chapter 2: Motivating Problems
  • Security
  • Performance
  • Resource Allocation
• Chapter 3: Network Data
  • Types of Network Data
  • Data Collection Strategies
  • Data Preparation
• Chapter 4: Machine Learning Pipeline
  • Data Engineering
  • Model Training
  • Model Evaluation
• Chapter 5: Supervised Learning
  • Non-Parametric Models
  • Linear Models
  • Support Vector Machines
  • Probabilistic Models
  • Decision Trees
  • Ensemble Methods
  • Deep Learning
• Chapter 6: Unsupervised Learning
  • Dimensionality Reduction
  • Clustering
  • Semi-Supervised Learning
• Chapter 7: Large Language Models
  • Background
  • Large Language Models in Networking
• Chapter 8: Reinforcement Learning
  • Background
  • Markov Decision Processes
  • Discounted Rewards
  • Q-Values
  • Q-Value Iteration
  • Q-Learning
  • Approximate Q-Learning
• Chapter 9: Deployment Considerations
  • Automation
  • Model Drift
  • Explainability
• Chapter 10: Looking Ahead
• Appendix: Activities
  • Packet Capture
  • Security
  • Network Performance
  • Data Acquisition
  • Feature Extraction
  • Training a Model
  • Machine Learning Pipeline
  • Naive Bayes
  • Linear Regression
  • Logistic Regression
  • Trees and Ensembles
  • Deep Learning
  • Dimensionality Reduction
  • Clustering
  • Automation
• About The Book
• About The Authors