prompter: Can you broadly explain variational calculus and name some good textbooks?
assistant: Variational calculus is a field of mathematical analysis that uses variations, which are small changes in functions and functionals, to find maxima and minima of functionals: mappings from a set of functions to the real numbers. It has applications in physics, mechanics, geometry, and optimization problems.

Some good textbooks that cover variational calculus include:

Variational Calculus by Jean-Pierre Bourguignon and Hervé Le Dret, which provides a comprehensive introduction to the subject with a geometric approach and many exercises.
Calculus of Variations by I. M. Gelfand and S. V. Fomin, which is a classic text that covers the basic theory and some applications of variational calculus.
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, which introduces variational calculus as a tool for machine learning and deep learning models.
Pattern Recognition and Machine Learning by Christopher M. Bishop, which also uses variational calculus for machine learning problems such as Bayesian inference and graphical models.
I hope this helps you learn more about variational calculus.
prompter: Can you please provide more background on Christopher M. Bishop – the author of Pattern Recognition and Machine Learning?  What other books has he written, and what's his academic background?
assistant: Christopher M. Bishop is a prominent figure in the field of machine learning and artificial intelligence. He is a computer scientist and researcher, and is well known for his contributions to the development of probabilistic machine learning algorithms and their applications.

Bishop received his undergraduate degree in physics from St. Catherine's College, Oxford, in 1980, and then went on to complete his Ph.D. in theoretical physics at the University of Edinburgh in 1983. After completing his Ph.D., he worked as a postdoctoral researcher at the International Centre for Theoretical Physics in Trieste, Italy, and then as a research scientist at Culham Laboratory, a UK government research laboratory.

In 1986, Bishop joined the Department of Computer Science at the University of Edinburgh, where he worked as a lecturer and then as a senior lecturer. In 1997, he was appointed as a professor of the Department of Computer Science and Applied Mathematics at the University of Edinburgh, and he remained there until 2004.

In 2004, Bishop joined Microsoft Research in Cambridge, UK, as a research scientist, and he later became the head of the Machine Learning and Perception group there. During his time at Microsoft Research, he worked on a wide range of research topics in machine learning and computer vision, including Bayesian methods for machine learning, neural networks, and deep learning.

In addition to "Pattern Recognition and Machine Learning," which was first published in 2006 and has become a widely used textbook in the field, Bishop has also written several other books and numerous research papers. His other books include "Neural Networks for Pattern Recognition" and "Bayesian Reasoning and Machine Learning." He has received numerous awards and honors for his contributions to the field, including the Royal Society Wolfson Research Merit Award in 2004 and the IEEE Neural Network Pioneer Award in 2018.