Machine Learning and AI with Simple Python and Matlab Scripts (eBook)

Courseware for Non-computing Majors

M. Umit Uyar (Autor)

eBook Download: EPUB

2025
594 Seiten
Wiley-IEEE Press (Verlag)
978-1-394-29496-1 (ISBN)

Infos

A practical guide to AI applications for Simple Python and Matlab scripts

Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular topics in engineering and science. Step-by-step instructions for simple Python and Matlab scripts mimicking real-life applications will enter the readers into the magical world of AI, without requiring them to have advanced math and computational skills. The book is supported by instructor only lecture slides and sample exams with multiple-choice questions.

Machine Learning and AI with Simple Python and Matlab Scripts includes information on:

• Artificial neural networks applied to real-world problems such as algorithmic trading of financial assets, Alzheimer's disease prognosis
• Convolution neural networks for speech recognition and optical character recognition
• Recurrent neural networks for chatbots and natural language translators
• Typical AI tasks including flight control for autonomous drones, dietary menu planning, and route planning
• Advanced AI tasks including particle swarm optimization and differential and grammatical evolution as well as the current state of the art in AI tools

Machine Learning and AI with Simple Python and Matlab Scripts is an accessible, thorough, and practical learning resource for undergraduate and graduate students in engineering and science programs along with professionals in related industries seeking to expand their skill sets.

M. Ümit Uyar is a Professor at the City College of the City University of New York, USA. Dr. Uyar is an IEEE Fellow, author, co-author and co-editor of seven books, holder of seven U.S. patents, and developer of AI and game theory-based algorithms for applications in topology control in mobile networks and personalized cancer treatment.

Textauszug

1
Introduction

1.1 Artificial Intelligence

Artificial intelligence (AI) has become one of the most exciting and active fields of research in recent years. AI attempts to develop software and computing devices that are able to perform tasks which have been associated only with thinking beings for centuries. Mimicking intelligence, creativity, deduction capability and ability to learn from experience are some of the directions that AI undertakes. AI's recent ubiquitous presence in everyone's life makes this technology an integral part of daily activities that is often taken for granted. Most people treat AI tools as black boxes (i.e. devices whose outputs are observable but whose internal details are unknown by users) which spit out solutions to make problems go away. A reader of this book will quickly appreciate that there is no magic behind the way AI operates. By understanding the mechanisms employed in many popular AI techniques, correct solutions are easily obtained for complicated tasks. Through hands‐on programming projects, the reader will be able to grasp how easily many otherwise unsolvable problems can be handled using AI.

1.2 A Historical Perspective

The question of whether a human‐created device can be intelligent goes back to the middle of the twentieth century when the so‐called Turing test was proposed to examine whether or not a machine is capable of thinking [1, 2]. The Turing test assumes that a human interrogator asks questions to both another human and a computer through an impediment that prevents identification of the responders. The proposition of the Turing test is that if the interrogator cannot distinguish which answers come from whom, then the interrogated human and the machine have the same intelligence. The Turing test has been used ever since to judge whether a computer can think. The term artificial intelligence was coined in 1955, six years after the Turing test was introduced, as the science and engineering for making intelligent machines together with the Turing test are considered as the beginning of modern AI [3].

The path from initial theoretical AI research to its realistic applications has progressed through many successful breakthroughs. One of the prominent milestones for engineers was marked by the introduction of the mobile robot Shaky in 1966 [4]. Shaky was controlled by so‐called intelligent algorithms that analysed its surroundings to carry out a plan for an intended goal. On another path, it has long been believed that being able to effectively play a chess game is a good indicator for the effectiveness of AI. IBM developed a chess‐playing computer called Deep Blue that defeated the chess grandmaster Garry Kasparov, the chess champion of the world at that time, in a historic match in 1997 [5]. Recent developments in self‐driving cars would not have been possible without AI‐based algorithms, which are essential for replicating the complex decision processes of a human driver [6]. In 2015, car maker Tesla marked one of the greatest milestones by announcing fully automated self‐driving vehicles. By equipping its cars with AI software for interpreting and understanding the visual world, Tesla showed that robust path planning and real‐time decision making were within reach [7]. These are only a few of the highlights marking recent developments in AI. It is widely believed that the importance of AI will only grow in years to come.

1.3 Principles of AI

An AI can be classified by the areas for which it is designed to find solutions. Currently, these areas include natural language processing, computer vision, automatic programming, robotics and intelligent data retrieval systems. However, grouping AI by the computational concepts employed for solving problems is a more accurate and better‐suited classification for the readers of this book.

Searching is one of the computational methods that can be used for various AI tasks such as reasoning (e.g. finding inference roles), planning (e.g. search through goals and sub‐goals) and moving (e.g. exploring a surrounding space by robots). Common AI tools to provide intelligent decisions in situations with incomplete or uncertain information can involve engaging Bayesian models, probabilistic algorithms and approaches derived from decision theory. Evolutionary algorithms are problem solving methods that attempt to replicate evolutionary operations of biological organisms in searching through a solution space. They are often used in AI to learn from previous experiences and to find solutions in large and complex settings. Learning principles of AI can be implemented using artificial neural networks and its variants, including convolutional neural networks and recurrent neural networks (RNNs), which allow for a system to be trained on a known set of data and for subsequent prediction of outcomes from new unknown events. Generative AI refers to a class of artificial intelligence systems that are designed to create new content, such as text, images, audio or code, by learning patterns from existing data. Unlike traditional AI, which often focuses on recognizing patterns or making predictions, generative AI models use those patterns to produce novel outputs that resemble the input data but are not mere copies. These systems are typically built using techniques like deep learning and neural networks, making them capable of generating realistic and creative content in various domains. Examples of generative AI methods include RNNs, long short term memory (LSTM) [8] and transformers [9].

1.4 Applications That Are Impossible Without AI

Starting from the early days of the twenty‐first century, AI has been used in countless applications ranging from solving a trivial task of suggesting emojis in text messages [10] to design of flight‐control software for commercial aeroplanes [11]. Some of the most prominent uses of AI are in the areas of medical diagnosis, image processing, control of autonomous vehicles and prediction of real‐life events. In many similar tasks, an abundance of data (e.g. camera input, radar reading, proximity sensor) has to be processed in real time, something at which AI is especially good.

Applications of AI can save lives, literally. In the healthcare field, high‐risk patients can be successfully monitored remotely with AI‐based voice assistants. Automated physician assistance systems guided by AI methods are capable of generating questions to best diagnose a patient [12] and in finding an optimal and personalized treatment for them [13,14]. During surgery, an AI‐based system can guide a surgeon's scalpel so that soft tissue damage is minimized and the resulting data collected during the procedures are quickly interpreted in clinical context [15].

AI can control an autonomous vehicle in places when a human operator cannot or does not want to operate. Self‐driving cars [16], unmanned ground vehicles, drones and underwater vehicles [17–19] are only a few examples where the advantages of AI are directly visible.

Predicting the future may be one of the most powerful desires for many of us. Although AI cannot find answers to important existential questions, it can be helpful in guessing the probabilities of many future events [20]. For example, in an attempt to prepare for earthquakes, a combination of evolutionary algorithms, swarm intelligence and artificial neural networks is being used to forecast possible future seismic events. The natural language processing methods of AI can be employed in economics to estimate upcoming changes in trading markets [21] and in judicial applications to predict the outcome of a court ruling based on court proceedings [22].

1.5 Organization of This Book

This book is a beginner's guide to exciting and modern applications of AI techniques. The reader will be introduced to the basic concepts and principles of AI in order to develop skills readily applicable to real engineering projects. Through step‐by‐step instructions, the reader will learn how to implement typical AI tasks including control of autonomous drones, speech and character recognition, natural language processing, dietary menu planning, optimal selections for project management tasks and maximizing profits in algorithmic stock trading. We believe that all readers, regardless of their area of expertise, will enjoy an empowerment stemming from seeing relatively simple AI procedures used to tackle otherwise formidable problems.

This book is written with a diverse spectrum of readers in mind: engineers, scientists, economists and all backgrounds of students ranging from engineering to liberal arts. It welcomes all readers who would like to enter the realm of AI‐based problem solving as it has become an essential skill for professionals in the twenty‐first century.

In Chapter 2, the general concepts of an artificial neural network (ANN) architecture and its training process are introduced. As one of the most popular tools in AI in handling tasks that mimic events recorded in large amounts of data, ANN‐based AI tools dominate the applications...

Produkt-Details

• Erscheint lt. Verlag: 17.3.2025
• Reihe/Serie: IEEE Press
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: ai matlab • AI python • Artificial Intelligence • Artificial Neural Networks • bio-inspired computation • convolutional neural networks • machine learning • MATLAB • neural network • NLP • OCR • Python • Recurrent Neural Network • Speech Recognition
• ISBN-10: 1-394-29496-4 / 1394294964
• ISBN-13: 978-1-394-29496-1 / 9781394294961