Linear Algebra and Probability for Computer Science Applications Download PDF MB. Based on the author's course at NYU, Linear. Linear Algebra And Probability For Computer Science Applications Ernest Davis [ PDF] [EPUB]. Ernest Davis is a computer science professor in. Linear Algebra And Probability For Computer Science Applications - [PDF] First Course in Probability (PDF) 9th Edition features clear and.
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Bird R, de Moor O Algebra of programming. Series in Computer Science. Baroni M, Zamparelli R Nouns are vectors, adjectives are matrices: representing adjective-noun constructions in semantic space. Conway JH Regular algebra and finite machines. Frias MF Fork algebras in algebra, logic and computer science.
Logic and computer science. In: Mathematical library, vol Gibbons J, Hinze R Just do it: simple monadic equational reasoning. Gibbons J Conditionals in distributive categories. Hehner E A probability perspective.
Hoogendijk P A generic theory of data types. Latouche G, Ramaswami V Introduction to matrix analytic methods in stochastic modeling. Lawvere B, Schanuel S Conceptual mathematics: a first introduction to categories. MacLane S Categories for the working mathematician.
In: Graduate texts in mathematics, vol 5. Springer, Berlin Google Scholar Mac Macedo H Matrices as arrows—why categories of matrices matter. Relating graph properties.
Eigenvalues, Eigenvectors. Independent set, Vertex cover, Network Flows, Cuts.
Linear Programming, Duality. Modular arithmetic.
Prime numbers. Fundamental theorem of arithmetic.
Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. Why Worry About The Maths? There are many reasons why the mathematics of Machine Learning is important and I will highlight some of them below: Selecting the right algorithm which includes giving considerations to accuracy, training time, model complexity, number of parameters and number of features.
Choosing parameter settings and validation strategies. Identifying underfitting and overfitting by understanding the Bias-Variance tradeoff. Estimating the right confidence interval and uncertainty.