The chapter introduces the concept of machine learning, highlighting its importance and historical development. It begins by explaining that current computer systems lack the ability to learn from past experiences or improve based on past mistakes, necessitating manual programming. Machine learning aims to address this by enabling computers to learn from data and adapt to changing environments. The chapter outlines three major periods in the history of machine learning: 1. **Neural Modelling and Decision-Theoretic Techniques**: Focuses on building general-purpose learning systems with initial structure or task-oriented knowledge. Examples include neural networks and decision-theoretic approaches. 2. **Symbolic Concept-Oriented Learning**: Emphasizes logic and graph structure representations, aiming to acquire symbolic descriptions of higher-level knowledge. This paradigm includes research on human concept acquisition and pattern recognition systems. 3. **Knowledge-Intensive Approaches**: Combines various learning strategies, emphasizing task-oriented knowledge and exploring alternative learning methods such as learning from instruction and analogy. The importance of machine learning is discussed, emphasizing its ability to handle complex tasks, extract hidden relationships in large datasets, improve existing machine designs, and adapt to changing environments. The chapter also provides an overview of the varieties of machine learning, including contributions from statistics, neural networks, and other fields. Finally, the chapter introduces a book titled "New Advances in Machine Learning," edited by Yagang Zhang, which aims to provide a comprehensive introduction to the principles and algorithms of machine learning. The book is intended for both academic and practical audiences, covering a wide range of machine learning approaches and providing useful bibliographical information.The chapter introduces the concept of machine learning, highlighting its importance and historical development. It begins by explaining that current computer systems lack the ability to learn from past experiences or improve based on past mistakes, necessitating manual programming. Machine learning aims to address this by enabling computers to learn from data and adapt to changing environments. The chapter outlines three major periods in the history of machine learning: 1. **Neural Modelling and Decision-Theoretic Techniques**: Focuses on building general-purpose learning systems with initial structure or task-oriented knowledge. Examples include neural networks and decision-theoretic approaches. 2. **Symbolic Concept-Oriented Learning**: Emphasizes logic and graph structure representations, aiming to acquire symbolic descriptions of higher-level knowledge. This paradigm includes research on human concept acquisition and pattern recognition systems. 3. **Knowledge-Intensive Approaches**: Combines various learning strategies, emphasizing task-oriented knowledge and exploring alternative learning methods such as learning from instruction and analogy. The importance of machine learning is discussed, emphasizing its ability to handle complex tasks, extract hidden relationships in large datasets, improve existing machine designs, and adapt to changing environments. The chapter also provides an overview of the varieties of machine learning, including contributions from statistics, neural networks, and other fields. Finally, the chapter introduces a book titled "New Advances in Machine Learning," edited by Yagang Zhang, which aims to provide a comprehensive introduction to the principles and algorithms of machine learning. The book is intended for both academic and practical audiences, covering a wide range of machine learning approaches and providing useful bibliographical information.