Practical Artificial Intelligence: Machine Learning, Bots, and Agent Solutions Using C# Front Cover

Practical Artificial Intelligence: Machine Learning, Bots, and Agent Solutions Using C#

  • Length: 690 pages
  • Edition: 1st ed.
  • Publisher:
  • Publication Date: 2018-07-12
  • ISBN-10: 1484233565
  • ISBN-13: 9781484233566
  • Sales Rank: #1070177 (See Top 100 Books)
Description

Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects.

People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions. Rather than focusing on theory and overly scientific language, this book will enable practitioners of all levels to not only learn about AI but implement its practical uses.

What You’ll Learn

  • Understand agents and multi agents and how they are incorporated
  • How machine learning relates to real world problems and what it means to you
  • Apply supervised and unsupervised learning techniques and methods in the real world
  • Implement reinforcement learning, game programming, simulation, and neural networks

Who This Book Is For

Computer science students, professionals, and hobbyists interested in AI and its applications.

Table of Contents

Chapter 1: Logic & AI
Chapter 2: Automated Theorem Proving & First-Order Logic
Chapter 3: Agents
Chapter 4: Mars Rover
Chapter 5: Multi-Agent Systems
Chapter 6: Communication in a Multi-Agent System Using WCF
Chapter 7: Cleaning Agents: A Multi-Agent System Problem
Chapter 8: Simulation
Chapter 9: Support Vector Machines
Chapter 10: Decision Trees
Chapter 11: Neural Networks
Chapter 12: Handwritten Digit Recognition
Chapter 13: Clustering & Multi-objective Clustering
Chapter 14: Heuristics & Metaheuristics
Chapter 15: Game Programming
Chapter 16: Game Theory: Adversarial Search & Othello Game
Chapter 17: Reinforcement Learning

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