Introduction to Artificial Intelligence


Introduction to Artificial Intelligence

Introduction to Artificial Intelligence
Introduction to Artificial Intelligence

Artificial intelligence 

    Artificial intelligence (AI) is the simulation of AI functions by machines, particularly computer systems. Expert systems, natural language processing, speech recognition, and machine vision are some examples of specific AI applications.

How does AI function?

    Vendors have been rushing to showcase how their goods and services use AI as the hype surrounding AI has grown. Frequently, what they mean by AI is just one element of AI, like machine learning. For the creation and training of machine learning algorithms, AI requires a foundation of specialized hardware and software. There is no one programming language that is exclusively associated with AI, but a handful are, including Python, R, and Java.
    A vast volume of labelled training data is typically ingested by AI systems, which then examine the data for correlations and patterns before employing these patterns to forecast future states.

Internet-of-Things (IoT) Systems 


Table of Contents

1. Introduction

  • 1.1 What Is Artificial Intelligence?
  • 1.2 The History of AI
  • 1.3 AI and Society
  • 1.4 Agents
  • 1.5 Knowledge-Based Systems
2. Propositional Logic

  • 2.1 Syntax
  • 2.2 Semantics
  • 2.3 Proof Systems
  • 2.4 Resolution
  • 2.5 Horn Clauses
  • 2.6 Computability and Complexity
  • 2.7 Applications and Limitations 
  • 2.8 Exercises

3. First-order Predicate Logic
  • 3.1 Syntax
  • 3.2 Semantics 
  • 3.3 Quantifiers and Normal Forms
  • 3.4 Proof Calculi
  • 3.5 Resolution.
  • 3.6 Automated Theorem Provers
  • 3.7 Mathematical Examples
  • 3.8 Applications
4. Limitations of Logic
  • 4.1 The Search Space Problem
  • 4.2 Decidability and Incompleteness
  • 4.3 The Flying Penguin
  • 4.4 Modeling Uncertainty
  • 4.5 Exercises
5. Logic Programming with PROLOG
6. Search, Games and Problem Solving
7. Reasoning with Uncertainty
8. Machine Learning and Data Mining
9. Neural Networks
10. Reinforcement Learning
11. Solutions for the Exercises

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