True to form as the original “curious character,” legendary physicist Richard Feynman often broke out of his favored field of expertise to give his own special slant on other areas. One important case in point was when he gave a course at Caltech from 1983-1986 called “Potentialities and Limitations of Computing Machines.” The lectures were compiled into what’s become one of my favorite texts of all time – “The Feynman Lectures on Computation.” The book was published in 1996 by Feynman’s children Carl and Michelle.

Although over 25 years old now, most of the material is timeless and presents a “Feynmanesque” overview of many standard and some not-so-standard topics in computer science. These include the *Theory of Computation* including *Turing Machines*, information theory, Shannon’s Theorem, reversible computation, the thermodynamics of computation, and quantum computing. Taken together, these lectures represent a unique exploration into the fundamentals of computation.

The late Richard P. Feynman was Richard Chace Tolman Professor of Theoretical Physics at Caltech. Feynman made many fundamental contributions to physics, particularly to quantum electrodynamics, quantum field theory, and particle physics. He is best known for the development of Feynman diagrams and path integrals. Feynman shared the Nobel Prize in Physics in 1965 for his work on quantum electrodynamics.

A delightful review of this book was written in 1999 by Anthony (Tony) J.G. Hey, who worked with Feynman at Caltech in the early 1970s.

Feynman’s unique philosophy of learning and discovery comes through strongly in these lectures. He constantly points out the benefits of playing with concepts and working out solutions to problems on your own. As Feynman says in the lectures:

If you keep proving stuff that others have done, getting confidence, increasing the complexities of your solutions – for the fun of it – then one day you’ll turn around and discover that nobody actually did that one!”

And that’s the way to become a computer scientist, or in today’s terms – a Data Scientist!

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