Introduction to the Course

Welcome!

This course is specifically designed to provide you with a deep understanding of how to leverage graphs for developing artificial intelligence (AI) models. Throughout this course, you'll gain expertise in a powerful modeling technique called Bayesian networks (BN). Bayesian networks use the inductive mode of reasoning, allowing you to seamlessly integrate both sample data and expert-judgment information in a logical and consistent manner for making inferences.

Bayesian networks are graph-based tools that model expert knowledge by combining raw data and human insights. This approach is particularly useful when large datasets are unavailable, but expert knowledge can be harnessed to establish causal relationships. With their ability to manage uncertainty explicitly, BNs are versatile and applicable across various real-world problems, including risk assessment, bankruptcy prediction, product acceptability, medical diagnosis, and construction design process diagnosis, among others.

Throughout the course, we'll delve into the mathematical fundamentals of BNs, learning how to select appropriate parameters and create models. We'll discuss the criteria for evaluating network performance, ensuring usefulness, and avoiding bias. Additionally, we'll explore how structured information can be translated into BNs and discover heuristics and methodologies for building BN structures tailored to different modeling situations.

By the end of this extensive course, you'll be well-equipped to tackle a wide range of problems using BN, blending data and expert knowledge to generate powerful AI models with explicit knowledge representation.

The course is divided into three main parts, each designed to build on the knowledge and skills you acquire along the way. So, let's get started on this exciting journey to unlock the true potential of BN in the realm of Artificial Intelligence!

Part 1: Introduction to graphs

In this section, we'll provide you with a solid foundation in graph theory. We'll start by explaining graphs and their underlying logic. We will become familiar with essential Python libraries, such as NetworkX and Matplotlib, that are used for creating, manipulating, and visualizing graphs. We'll cover basic operations and features to help you understand different types of graphs, such as directed and undirected, cyclic and acyclic graphs, and various graph algorithms and properties. By the end of this part, you'll be well-versed in working with graphs and their applications.

We will kick off this journey by diving into the fundamental concepts and techniques that lay the foundation for more advanced topics later in the course. Here's a brief overview of what you can expect from the first two chapters:

• Chapter 1: Introduction to Graphs. This chapter introduces the course and compares machine learning with graph-based AI. It teaches graph creation and plotting in Python, along with different types of graphs, providing a comprehensive graph theory understanding.

• Chapter 2: Exploring Graphs Characteristics in Python. Here, we delve into graph characteristics and algorithms, studying degree distribution, degree centrality, and shortest path calculations. We also discuss betweenness centrality and node significance, summarizing the main concepts for reinforcement.

By the end of these two chapters, you'll have a strong foundation for analyzing graphs using Python, setting the stage for more advanced topics and techniques in the following lessons.

Part 2: Creating Bayesian networks

Here, we'll transition from graphs to AI, focusing on directed acyclic graphs and conditional probability as the foundation for BNs. We'll show you how to create BNs in Python, starting with simple examples to teach the main concepts. You'll learn about the key features and functionalities of BNs, becoming comfortable with their basics in both theory and practice.

• Chapter 3: Bayesian Networks. This chapter introduces BNs, a kind of directed acyclic graph, starting with conditional probability and causality basics. It explains BNs and its uses, teaches descriptive graph transformation into BNs, and guides to create the first BN in Python.

• Chapter 4: Graph Patterns in Bayesian Networks. We explore common graph patterns in BNs, teaching creation and analysis of common-cause, causal-chain, and common-effect graphs. It guides you from building a simple BN model starting from a causal structure to introducing synthetic nodes and key hyperparameters for training a BN.

• Chapter 5: Structure-Based Hyperparameters in Bayesian Networks. Diving into the BN's critical features and hyperparameters, it covers aspects like input nodes, synthetic nodes, and target node states. It discusses data structuring pre-learning, and model output analysis, and provides tips for improving your BN model.

With these three lessons, you'll gain a deeper understanding of graph patterns in BNs and develop the skills to fine-tune your models using the right features and hyperparameters. This chapter includes a quiz, and as a practical exercise, you'll work on a mini-project where you'll convert a causality diagram into a graph-based BN.

Part 3: Advanced Bayesian networks

In this final part of the course, our aim is to equip you with the skills necessary to develop complex BN models. We'll dive into more sophisticated techniques and share a comprehensive toolbox for creating models while addressing common challenges you might encounter during the process. After completing this section, you'll be well-prepared to create graph AI models for complex situations, such as those commonly faced by data scientists in their work.

As we approach the final lessons of this comprehensive course on graph-based Artificial Intelligence models, let's explore what's in store for you:

• Chapter 6: Data-Based Bayesian Networks. Focusing on feature optimization for BN and data collection, it includes learning about AIC and BIC curves, various learning algorithms for parameter and structure learning, and inference algorithms used for graph training.

• Chapter 7: Building a Complex Bayesian Network. Guiding through data preparation for complex BN creation, it provides instructions to build, train, and test a complex model in Python, ending with a quiz and a summary. The practical exercise involves creating a knowledge and data-based BN in a mini-project.

• Chapter 8: Evaluating the Output and Performance. The final chapter discusses learning rate evaluation using ROC curves and accuracy metrics, interpreting BN output, influence analysis, and creating scenarios for decision-making based on the BN model.

By the end of this course, you'll be well-equipped to create, optimize, and evaluate complex BN models. With the skills and knowledge you've gained throughout the course, you'll be ready to tackle real-world problems using graph-based AI models!

Additionally, throughout the course, we'll provide ample examples, quizzes, and exercises to reinforce your learning and ensure you're ready to tackle real-world problems using graph-based AI models. By the end of this course, you'll be a confident practitioner of graph-based AI, capable of applying your skills to a wide range of situations in the realm of data science.

Prerequisites

We assume learners taking this course have a foundational understanding of Python programming, i.e., they can define variables, use control structures like if/else statements, execute loops, and create functions. They should also have a firm grasp of basic data structures and algorithms. A strong understanding of mathematical concepts, such as probability theory, statistics, and graph theory, will prove beneficial. Having prior knowledge or experience with Python machine-learning concepts and algorithms will be an advantage. Familiarity with Python libraries like NumPy, pandas, and matplotlib will aid in comprehending the coding aspects. The course naturally leads learners from basic to advanced topics, building conceptually step by step.