Machine Learning
2nd Edition
9364446038
·
9789364446037
© 2027 | Published: June 22, 2026
Overview:This book is a foundational text in the field of machine learning. The book provides a comprehensive introduction to the principles, algorithms, and methodologies that underpin machine learning, making it suitable for both beginners and expe…
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Chapter 1: Introduction
Chapter 2: Concept Learning and the General-to- Specific Ordering
Chapter 3: Instance-Based Learning
Chapter 4: Decision Tree Learning
Chapter 5: Evaluating Hypotheses
Chapter 6: Bayesian Learning
Chapter 7: Computational Learning Theory
Chapter 8: Artificial Neural Networks
Chapter 9: Deep Neural Network Architectures
Chapter 10: Learning Sets of Rules
Chapter 11: Analytical Learning
Chapter 12: Combining Inductive and Analytical Learning
Chapter 13: Reinforcement Learning
Chapter 14: Genetic Algorithms
Chapter 15: Unsupervised Learning
Chapter 16: Support Vector Machines and Kernel Methods
Chapter 17: Natural Language Processing
Chapter 2: Concept Learning and the General-to- Specific Ordering
Chapter 3: Instance-Based Learning
Chapter 4: Decision Tree Learning
Chapter 5: Evaluating Hypotheses
Chapter 6: Bayesian Learning
Chapter 7: Computational Learning Theory
Chapter 8: Artificial Neural Networks
Chapter 9: Deep Neural Network Architectures
Chapter 10: Learning Sets of Rules
Chapter 11: Analytical Learning
Chapter 12: Combining Inductive and Analytical Learning
Chapter 13: Reinforcement Learning
Chapter 14: Genetic Algorithms
Chapter 15: Unsupervised Learning
Chapter 16: Support Vector Machines and Kernel Methods
Chapter 17: Natural Language Processing
Overview:
This book is a foundational text in the field of machine learning. The book provides a comprehensive introduction to the principles, algorithms, and methodologies that underpin machine learning, making it suitable for both beginners and experienced practitioners. The book systematically explains key topics such as supervised learning, unsupervised learning, reinforcement learning, and decision tree algorithms, while also delving into concepts like neural networks, Bayesian learning, and genetic algorithms. The text is notable for its clear explanations and practical approach, offering mathematical rigor alongside real world examples to help readers understand how machine learning can be applied to solve complex problems.
This Indian adaptation introduces four new chapters, deep learning, unsupervised learning, natural language processing, and support vector machines & Kernel methods.
Key Features:
1. Comprehensive textbook aligning with the syllabus of various Indian universities.
2. Covers both foundational theories of machine learning and related advanced machine learning topics.
3. Use practical examples and applications to explain how machine learning concepts are applied in the real world.
4. Offers a solid base in the necessary mathematical principles, particularly in statistics and probability theory.
5. New chapters on deep learning, unsupervised learning, natural language processing, and support-vector machines & Kernel methods.
This book is a foundational text in the field of machine learning. The book provides a comprehensive introduction to the principles, algorithms, and methodologies that underpin machine learning, making it suitable for both beginners and experienced practitioners. The book systematically explains key topics such as supervised learning, unsupervised learning, reinforcement learning, and decision tree algorithms, while also delving into concepts like neural networks, Bayesian learning, and genetic algorithms. The text is notable for its clear explanations and practical approach, offering mathematical rigor alongside real world examples to help readers understand how machine learning can be applied to solve complex problems.
This Indian adaptation introduces four new chapters, deep learning, unsupervised learning, natural language processing, and support vector machines & Kernel methods.
Key Features:
1. Comprehensive textbook aligning with the syllabus of various Indian universities.
2. Covers both foundational theories of machine learning and related advanced machine learning topics.
3. Use practical examples and applications to explain how machine learning concepts are applied in the real world.
4. Offers a solid base in the necessary mathematical principles, particularly in statistics and probability theory.
5. New chapters on deep learning, unsupervised learning, natural language processing, and support-vector machines & Kernel methods.