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 exer-
cises 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.
BERNHARD MEHLIG is Professor in Complex Systems at the University of
Gothenburg, Sweden. His research is focused on statistical physics of complex sys-
tems and fluid mechanics, and he has published extensively in these areas. In 2010,
he was awarded the prestigious Göran Gustafsson Prize in physics for his research
in statistical physics. He has taught a course on machine learning for more than 15
years at Chalmers University of Technology and at the University of Gothenburg.
MACHINE LEARNING WITH NEURAL
NETWORKS
An Introduction for Scientists and Engineers
BERNHARD MEHLIG
University of Gothenburg, Sweden
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Machine Learning with Neural Networks_ An Introduction for Scientists and Engineers_Bernhard Mehlig.pdf