# Linear Algebra and Probability for Computer Science by Ernest Davis

By Ernest Davis

Features

Focuses on mathematical suggestions which are such a lot suitable to laptop scientists
Assumes as little mathematical history as possible
Covers purposes from special effects, internet seek, laptop studying, cryptography, and a number of alternative desktop technology areas
Includes MATLAB capabilities, MATLAB programming assignments, and difficulties in every one chapter
Offers MATLAB code at www.cs.nyu.edu/faculty/davise/MathTechniques/index.html

Based at the author’s direction at NYU, Linear Algebra and chance for desktop technology functions provides an advent to 2 mathematical fields which are primary in lots of components of desktop technology. The path and the textual content are addressed to scholars with a truly vulnerable mathematical heritage. lots of the chapters talk about appropriate MATLAB® capabilities and contours and provides pattern assignments in MATLAB; the author’s site offers the MATLAB code from the book.

After an introductory bankruptcy on MATLAB, the textual content is split into sections. The part on linear algebra offers an creation to the speculation of vectors, matrices, and linear ameliorations over the reals. It contains an in depth dialogue on Gaussian removal, geometric functions, and alter of foundation. It additionally introduces the problems of numerical balance and round-off mistakes, the discrete Fourier remodel, and singular price decomposition. The part on likelihood offers an creation to the elemental thought of likelihood and numerical random variables; later chapters speak about Markov versions, Monte Carlo tools, info concept, and uncomplicated statistical ideas. the point of interest all through is on themes and examples which are rather appropriate to desktop technology functions; for instance, there's an in depth dialogue at the use of hidden Markov types for tagging textual content and a dialogue of the Zipf (inverse energy legislations) distribution.

Examples and Programming Assignments
The examples and programming assignments concentrate on desktop technological know-how functions. The purposes lined are drawn from a variety of laptop technology parts, together with special effects, laptop imaginative and prescient, robotics, normal language processing, net seek, computer studying, statistical research, online game taking part in, graph conception, clinical computing, choice concept, coding, cryptography, community research, information compression, and sign processing.

Homework Problems
Comprehensive challenge sections comprise conventional calculation workouts, concept difficulties corresponding to proofs, and programming assignments that contain developing MATLAB functions.

http://www.cs.nyu.edu/faculty/davise/MathTechniques/MoreAssigs/MoreAssigs.html

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Extra resources for Linear Algebra and Probability for Computer Science Applications

Example text

Suppose that you start with a value of y 0 in double precision and compute the series y 0 , y 1 , y 2 , . .. Given your conjecture in (b), how many terms of the series can you compute before the values become completely unrelated to the true value? This page intentionally left blank I Linear Algebra This page intentionally left blank Chapter 2 Vectors Linear algebra is the study of vectors, discussed in this chapter, and matrices, discussed in Chapter 3. 1 Definition of Vectors An n-dimensional vector is a n-tuple of numbers.

4. Dot Product 23 for Gristede’s then s − g is the amount they save at Gristede’s for each item. 8 · s. To convert a price vector from dollars to euros, we multiply by the current exchange rate. Other applications work similarly. 4 Dot Product The dot product of two n-dimensional vectors is computed by multiplying corresponding components, and then adding all these products. That is, v • w = v [1] · w [1] + . . + v [n] · w [n]. For example, 〈3, 1, 10〉 • 〈−2, 0, 4〉 = (3 · −2) + (1 · 0) + (10 · 4) = −6 + 0 + 40 = 34.

In actual information retrieval systems, the definition of document vector is a little more complex. It gives different weights to different words, depending on their frequency; more common words are considered 19 20 2. Vectors less important. Specifically, let N be the number of documents in a given collection. For any word w, let m w be the number of documents in the collection that contain w, and let i w = log(N /m w ), called the inverse document frequency. Note that the fewer the documents that contain w, the larger i w ; for common words such as “the,” which are found in all documents, i w = 0.