Machine Learning with Neural Networks

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

• Accessible and self-contained approach to the theoretical foundations of machine learning, and its modern applications in science and engineering
• Suitable for teaching advanced undergraduate or graduate courses on this topic for science, mathematics and engineering students
• Numerous end of chapter exercises expand and reinforce key concepts