Hot Best Seller

Linear Algebra and Probability for Computer Science Applications

Availability: Ready to download

Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB(R) functions and features and g Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB(R) functions and features and give sample assignments in MATLAB; the author's website provides the MATLAB code from the book. After an introductory chapter on MATLAB, the text is divided into two sections. The section on linear algebra gives an introduction to the theory of vectors, matrices, and linear transformations over the reals. It includes an extensive discussion on Gaussian elimination, geometric applications, and change of basis. It also introduces the issues of numerical stability and round-off error, the discrete Fourier transform, and singular value decomposition. The section on probability presents an introduction to the basic theory of probability and numerical random variables; later chapters discuss Markov models, Monte Carlo methods, information theory, and basic statistical techniques. The focus throughout is on topics and examples that are particularly relevant to computer science applications; for example, there is an extensive discussion on the use of hidden Markov models for tagging text and a discussion of the Zipf (inverse power law) distribution. Examples and Programming Assignments The examples and programming assignments focus on computer science applications. The applications covered are drawn from a range of computer science areas, including computer graphics, computer vision, robotics, natural language processing, web search, machine learning, statistical analysis, game playing, graph theory, scientific computing, decision theory, coding, cryptography, network analysis, data compression, and signal processing. Homework Problems Comprehensive problem sections include traditional calculation exercises, thought problems such as proofs, and programming assignments that involve creating MATLAB functions.


Compare

Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB(R) functions and features and g Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB(R) functions and features and give sample assignments in MATLAB; the author's website provides the MATLAB code from the book. After an introductory chapter on MATLAB, the text is divided into two sections. The section on linear algebra gives an introduction to the theory of vectors, matrices, and linear transformations over the reals. It includes an extensive discussion on Gaussian elimination, geometric applications, and change of basis. It also introduces the issues of numerical stability and round-off error, the discrete Fourier transform, and singular value decomposition. The section on probability presents an introduction to the basic theory of probability and numerical random variables; later chapters discuss Markov models, Monte Carlo methods, information theory, and basic statistical techniques. The focus throughout is on topics and examples that are particularly relevant to computer science applications; for example, there is an extensive discussion on the use of hidden Markov models for tagging text and a discussion of the Zipf (inverse power law) distribution. Examples and Programming Assignments The examples and programming assignments focus on computer science applications. The applications covered are drawn from a range of computer science areas, including computer graphics, computer vision, robotics, natural language processing, web search, machine learning, statistical analysis, game playing, graph theory, scientific computing, decision theory, coding, cryptography, network analysis, data compression, and signal processing. Homework Problems Comprehensive problem sections include traditional calculation exercises, thought problems such as proofs, and programming assignments that involve creating MATLAB functions.

30 review for Linear Algebra and Probability for Computer Science Applications

  1. 4 out of 5

    TΞΞL❍CK Mith!lesh

  2. 4 out of 5

    Vlad Korolev

  3. 5 out of 5

    Jovany Agathe

  4. 4 out of 5

    John

  5. 4 out of 5

    Ian Bowman

  6. 4 out of 5

    Naveen

  7. 4 out of 5

    Rani M

  8. 4 out of 5

    Sujana

  9. 5 out of 5

    Christian Ramsey

  10. 5 out of 5

    Benson Olutayo

  11. 4 out of 5

    Nahom Jareehcner

  12. 5 out of 5

    Juk

  13. 4 out of 5

    Pramitha Santhumayor

  14. 4 out of 5

    Nidhi Joshi

  15. 4 out of 5

    Rahul Bhardwaj

  16. 4 out of 5

    Ed

  17. 5 out of 5

    Mariusz Antk

  18. 5 out of 5

    Federico Carrone

  19. 4 out of 5

    Belkis Kapic

  20. 5 out of 5

    Said Ehsan

  21. 5 out of 5

    Hanimsofiaa

  22. 5 out of 5

    Jeremy Banta

  23. 4 out of 5

    Jeremy Banta

  24. 4 out of 5

    Stephen Fox

  25. 5 out of 5

    Divij Oberoi

  26. 5 out of 5

    Ali Musa

  27. 5 out of 5

    S B

  28. 5 out of 5

    Toshith

  29. 5 out of 5

    Shawn

  30. 5 out of 5

    Maciej Lewandowski

Add a review

Your email address will not be published. Required fields are marked *

Loading...