Bayes, AI and Deep Learning

Foundations of Data Science

Preface

Welcome to the fascinating world of Bayesian learning, artificial intelligence, and deep learning! This book is your guide to understanding these powerful tools and their applications, drawing from our experience teaching these exciting fields to two distinct audiences: business school students at the University of Chicago and engineers at George Mason University.

This unique blend of perspectives allows us to present complex concepts in a way that is accessible to data scientists, business professionals, and technical experts alike. Whether you’re a manager seeking to leverage AI in your organization or an engineer building the next generation of intelligent systems, this book has something for you.

The techniques discussed in this book are transformative and have a profound impact on automation. From self-driving cars to virtual assistants, they are already woven into daily life and will soon become even more pervasive across industries. Understanding them is essential for anyone who wants to stay ahead of the curve.

AI’s ability to learn, adapt, and make decisions accelerates automation across industries. By analyzing vast amounts of data, AI algorithms identify patterns and trends that support informed decision-making, leading to better resource allocation, optimized processes, and improved outcomes. For example, AI-powered chatbots handle customer inquiries around the clock, offering personalized, efficient support that boosts satisfaction and loyalty. AI also creates entirely new business models, disrupting traditional markets and unlocking opportunities for innovation and growth. In addition, it is driving progress in areas such as personalized medicine and space exploration, with the potential to revolutionize these fields and improve our lives.

The term AI has morphed over time. Coined in 1956 by John McCarthy, it was first defined as “the science and engineering of making intelligent machines.” The field has since evolved, and so has the definition. Today, AI is a broad field encompassing various subfields, including machine learning, deep learning, and natural language processing. These terms are often used interchangeably, but they are not synonymous. Machine learning is a subfield of AI that focuses on algorithms that learn from data. Deep learning is a subfield of machine learning that uses artificial neural networks to learn complex patterns. Natural language processing is a subfield of AI that focuses on algorithms that can understand and generate human language.

Since 1956, artificial intelligence has undergone significant transformations. Traditional AI focused on rule-based systems and Boolean logic with limited learning capabilities, which made them brittle in changing environments. In contrast, emerging AI is centered on modeling uncertainty, pattern matching, and deep learning—all data-driven approaches. These methods are more adaptable to complex, unstructured data, but they are also more data-dependent and can lack interpretability.

Old AI

If rain outside, then take umbrella

This rule cannot be learned from data. It does not allow inference. Cannot say anything about rain outside if I see an umbrella.

 

New AI

Probability of taking umbrella, given there is rain

Conditional probability rule can be learned from data. Allows for inference. We can calculate the probability of rain outside if we see an umbrella.

This book is based on lecture notes from our courses, refined and expanded over years of teaching. We have incorporated valuable feedback from students at both the University of Chicago and George Mason University to create a comprehensive and engaging learning experience. The book is organized into three parts:

• Part 1: Bayesian Learning: This section covers the basics of probability and Bayesian inference.
• Part 2: Artificial Intelligence: It explores the core concepts of AI and focuses on pattern-matching techniques such as decision trees and generalized linear models.
• Part 3: Deep Learning: It delves into the world of deep learning, focusing on the architecture and training of deep neural networks. It covers convolutional neural networks, recurrent neural networks, and generative adversarial networks.

This work is inspired by the contributions of many great thinkers in AI and machine learning. We acknowledge the foundational work of pioneers such as Claude Shannon (information theory), John von Neumann (game theory and decision science), and Richard Bellman (dynamic programming and optimal control).

The evolution of AI can be summarized in three stages:

1. Search. Early search engines answered a single question with a ranked list of webpages. The PageRank algorithm, developed by Larry Page and Sergey Brin, used power iterations to rank these pages by relevance. Statistical tools like Kendall’s tau and Spearman’s rank correlation measured the similarity between the ranking and actual relevance.
2. Suggestions. The first popular suggestion algorithm was developed by Netflix. It used collaborative filtering to recommend movies to users based on their viewing history and that of others, easing the burden of choice.
3. Summaries. Current AI systems like ChatGPT and Perplexity excel at summarization and generalization. These large language models distill vast amounts of complex information into clear, coherent summaries that capture the essential points. They can generalize across different domains, connecting concepts and providing insights that might not be immediately obvious. This ability represents a significant leap from simple search and recommendation, as these AI agents can now act as intelligent intermediaries that understand context, identify patterns, and present information in the most useful ways.

Initially, the interaction was a single question leading to a single answer. Then came suggestions, where the system anticipated user needs. Now, we have summaries, where AI agents interpret a request, formulate a plan, and execute it. This is the future of AI, where agents can work together to solve complex problems and provide valuable insights.

Bayesian learning is a powerful statistical framework based on the work of Thomas Bayes. It provides a probabilistic approach to reasoning and learning, allowing us to update our beliefs about the world as we gather new data. This makes it a natural fit for artificial intelligence, where we often need to deal with uncertainty and incomplete information. Artificial intelligence (AI) is a vast field that seeks to create intelligent agents capable of performing tasks that typically require human intelligence. These tasks can include perception, reasoning, learning, problem-solving, decision-making, and language processing. AI has made significant progress in recent years, driven by advances in computing power, data availability, and algorithms. Deep learning is a subfield of AI that uses artificial neural networks to learn from data. These networks are inspired by the structure and function of the human brain and have the ability to learn complex patterns and relationships in data. Deep learning has achieved remarkable results in various tasks such as image recognition, natural language processing, and machine translation.

The worlds of business and engineering are increasingly intertwined, as AI becomes an essential tool in both domains. This book bridges the gap between these disciplines by demonstrating how Bayesian learning, AI, and deep learning can be applied to address real-world challenges in:

• Business: Market analysis, customer segmentation, risk management, and strategic decision-making.
• Engineering: Robotics, image recognition, natural language processing, and data-driven automation.

Key Features of This Book:

• Accessible explanations: We break down complex concepts into manageable chunks, using real-world examples and analogies to illustrate key principles.
• Case studies: We showcase practical applications of Bayesian learning, AI, and deep learning across diverse industries.
• Hands-on exercises: We provide practical exercises and code examples to help you apply the concepts covered in the book to your own projects.

Joining the AI Revolution:

The field of AI is rapidly evolving, and this book equips you with the knowledge and skills to stay ahead of the curve. Whether you’re looking to enhance your business acumen or advance your engineering career, understanding the power of Bayesian learning, AI, and deep learning is crucial.

We invite you to join us on this exciting journey and discover the transformative potential of these powerful tools!