This post will further your knowledge in areas that all mathematicians will be interested in. Find out what resources you can read to learn more, and find a selection of useful links including a variety of ebooks, learning platforms, videos, tools and lecture notes on a wide array of topics, such as doing math foundation, set theory, logic, type theory, algebra and many many more.
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This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A.
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In this post, we’ve covered a pretty long list of free ebooks covering various topics such as Programming, Python, API Design, Data Science, Artificial Intelligence, Machine & Deep Learning, Probability & Statistics, Algebra, Algorithms and many more.
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This text contains sufficient material for a one-semester course in mathematical algorithms, for second year mathematics students. The course requires some exposure to the basic concepts of discrete mathematics, but no computing experience.
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These books are at the first-year graduate level or a little higher, depending on one’s university. The list includes basic & advanced algrebra and basic & advanced real analysis.
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These lecture notes were translated from the Finnish lecture notes for the TUT course ‘Matemaattinen kryptologia’. The laborious bulk translation was taken care of by the students Jussi Kangas (visiting from the University of Tampere) and Paul Coughlan (visiting from the University of Dublin, Trinity College).
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This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods.
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The way to get an intuitive understanding of the mathematics and of the physics is to analyze your solution thoroughly. Does it make sense? There are almost always several parameters that enter the problem, so what happens to your solution when you push these parameters to their limits?
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