A First Course in Artificial Intelligence

Osondu Oguike

• Publisher: Bentham Science Publishers
• Year: 2021
• ISBN:
  9781681088532
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

The importance of Artificial Intelligence cannot be over-emphasised in current times, where automation is already an integral part of industrial and business processes. A First Course in Artificial Intelligence is a comprehensive textbook for beginners which covers all the fundamentals of Artificial Intelligence. Seven chapters (divided into thirty-three units) introduce the student to key concepts of the discipline in simple language, including expert system, natural language processing, machine learning, machine learning applications, sensory perceptions (computer vision, tactile perception) and robotics. Each chapter provides information in separate units about relevant history, applications, algorithm and programming with relevant case studies and examples. The simplified approach to the subject enables beginners in computer science who have a basic knowledge of Java programming to easily understand the contents. The text also introduces Python programming language basics, with demonstrations of natural language processing. It also introduces readers to the Waikato Environment for Knowledge Analysis (WEKA), as a tool for machine learning. The book is suitable for students and teachers involved in introductory courses in undergraduate and diploma level courses which have appropriate modules on artificial intelligence.

Machine Learning

Osondu Oguike

Abstract

All discoveries made by man in any discipline, like physical sciences, biological sciences, social sciences, engineering, etc., are based on past experiences or collected data. This means that human beings solve the problem by using past experiences or collected data. Therefore, since one aspect of Artificial Intelligence, as pointed out in chapter 1, unit 1, is to design systems that act like man, it becomes necessary that computer systems should be designed to solve the problem the way human beings solve a problem. This means that computer systems should be designed to solve the problem using past experience or previously stored data. The data are called training data because they are used to train the computer to learn the trend or pattern of the training data. Learning the pattern or trend of the training data as a rule, it uses the learnt rule to solve a subsequent problem using test data that has the same structure as the training data. The vast amount of data in machine learning is divided into two sets, which are the training set and the test set. The training set is used to develop a model, while the test set is used to evaluate the performance of the model. Data splitting technique in machine learning refers to the technique used to split the data into a training set and test set. The aim is to avoid poor generalization, i.e., overfitting or overtraining. Using more training sets improves the accuracy of the model, while using more test data improves the accuracy of the error estimate. An appropriate training/test set ratio of 70:30 is considered appropriate. Machine learning, therefore, is an aspect of Artificial Intelligence that deals with the design of systems that uses a large set of data called training data to solve a particular problem. Machine learning is a broad area in Artificial Intelligence, which will be considered in the various units of this chapter.

Keywords: Classification algorithm, Data pre-processing, Decision tree algorithm, Feature engineering, K-means clustering algorithm, Learner’s input, Learner’s output, Naive Bayes algorithm, Regression algorithm.

1. INTRODUCTION TO MACHINE LEARNING

Human beings possess the ability to adapt, i.e., convert experience (data) into knowledge (output). In this context, data refers to the basic facts, while knowledge refers to the ability to use the basic fact to solve problems. God created human beings with a well-developed ability to turn experience (data) into knowledge. Therefore, in order to program computers to convert experience (data)

into knowledge, you need to understand some fundamental concepts involved in converting data into knowledge. These fundamental learning concepts are discussed in this unit.

1.1. Fundamentals of Machine Learning

A formal definition of mac...

Table of contents

• Welcome
• Table of Content
• Title
• BENTHAM SCIENCE PUBLISHERS LTD.
• PREFACE
• Introduction to Artificial Intelligence
• Expert System
• Natural Language Processing
• Machine Learning
• Machine Learning Applications
• Sensory Perception
• Robotics