Probability and Statistics for Computer Science
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Probability and Statistics for Computer Science
Probability and Statistics for Computer Science provides a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.
Probability and Statistics for Computer Science
Numéro d'article: 73194537

Probability and Statistics for Computer Science

Numéro d'article: 73194537

XPF 10284

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Probability and Statistics for Computer Science provides a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.
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Ce qui se démarque

Comprehensive Coverage
Covers essential probability and statistics concepts tailored for computer science applications, ensuring learners acquire relevant skills for data analysis, machine learning, and algorithm development.
Practical Examples
Incorporates real-world examples and projects, bridging theoretical concepts with practical implementation, enhancing students' understanding and application of statistical methods in computing.
Accessible Format
Designed for a diverse audience, the book simplifies complex theories and includes illustrations, making advanced statistical concepts approachable for students with varying backgrounds.

Détails du produit

Shop Probability and Statistics for Computer Science online at a best price in French Polynesia. 3319644092
  • This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features:•   A treatment of random variables and expectations dealing primarily with the discrete case.•   A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains.•   A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing.•   Achapter dealing with classification, explaining why it’s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors.•   A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems.•   A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis.•   A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals.Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know.  Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.
Publisher Springer
Publication date February 20, 2018
Edition 1st ed. 2018
Language English
Print length 391 pages
ISBN-10 3319644092
ISBN-13 978-3319644097
Item Weight 3.1 pounds (1.41 kg)
Dimensions 8.27 x 1.03 x 10.98 inches (21 x 2.6 x 27.9 cm)

À qui est-ce destiné ?

Suitable For
  • Computer Science Students

    Ideal for students studying computer science nuances in probability and statistics for algorithmic and data analysis.

  • Data Analysts

    Great resource for data analysts looking to strengthen their statistical foundation and understanding for data interpretation.

  • Machine Learning Enthusiasts

    Highly beneficial for those entering machine learning, providing insights into probability models and statistical methodologies.

Not Suitable For
  • Complete Beginners

    Individuals with no prior knowledge of statistics may struggle with the concepts without foundational background.

  • Advanced Statisticians

    Experienced statisticians may find the content basic and lacking depth for advanced statistical theories and applications.

  • Casual Learners

    Not suitable for those seeking a light introduction; it’s focused and requires commitment to comprehend effectively.

DESCRIPTION DU PRODUIT

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Questions et réponses des clients

  • question: What topics are covered in Probability and Statistics for Computer Science?

    répondre: This book provides an extensive overview of key concepts such as probability theory, statistical inference, hypothesis testing, and regression analysis. It critically examines the mathematical foundations necessary for data analysis and offers practical applications relevant to computer science. Students and professionals will find it beneficial for understanding machine learning algorithms and data-driven decision making.
  • question: Who is the target audience for this book?

    répondre: The book is primarily aimed at computer science students and professionals who wish to enhance their understanding of probability and statistics. It’s also suitable for researchers looking to apply statistical methods in their work. With its clear explanations and practical examples, it serves as an excellent resource for anyone interested in data science, machine learning, or quantitative analysis.
  • question: Is the book suitable for beginners in statistics and probability?

    répondre: Yes, Probability and Statistics for Computer Science is designed to be accessible to beginners. The author begins with fundamental concepts, progressively building up to more complex topics. Clear definitions, examples, and exercises help facilitate a smooth learning curve, making it ideal for novices who are eager to learn data analysis principles.
  • question: Does the book include practical exercises or examples?

    répondre: Absolutely! The book is replete with practical exercises and real-world examples. These not only reinforce the theoretical concepts discussed but also provide opportunities for hands-on practice. Readers can apply statistical methods to actual data sets, improving their understanding and skills in data analysis and interpretation.
  • question: What software or tools does this book recommend for statistical analysis?

    répondre: The book recommends popular statistical software tools such as R and Python, which are widely used for statistical analysis in the field of computer science. It provides examples of how to implement the statistical concepts through coding, making it a valuable guide for those looking to develop practical programming skills alongside theoretical knowledge.
  • question: Can this book help with preparing for data science interviews?

    répondre: Yes, this book can serve as a valuable resource for preparing for data science interviews. It covers essential probability and statistics topics often discussed in technical interviews. By mastering the concepts outlined in this book, candidates can approach interview questions with confidence and demonstrate their analytical skills effectively.
  • question: Are there any online resources or supplementary materials available with this book?

    répondre: Yes, many versions of this book include supplemental online resources such as lecture notes, additional exercises, and video tutorials. The availability of these materials can enrich the learning experience, providing further clarification on difficult topics and enhancing understanding through varied instructional methods.
  • question: How does this book compare to other statistics books for computer science?

    répondre: Probability and Statistics for Computer Science stands out due to its targeted approach towards computer scientists. In contrast to more general statistics books, it integrates examples from computer science and focuses on applications relevant to the field. This specialized perspective ensures that readers can directly apply the concepts in their projects and work scenarios.
  • question: Can educators use this book for teaching courses on statistics?

    répondre: Certainly, this book can be an excellent primary or supplementary text for teaching statistics in computer science. Its structured format, combined with clear explanations and examples, makes it a suitable choice for classroom settings. Educators can utilize its exercises and practical applications to enhance student engagement and comprehension of the material.
  • question: Where can I buy Probability and Statistics for Computer Science 1st ed. 2018 Edition in French Polynesia?

    répondre: You can purchase Probability and Statistics for Computer Science 1st ed. 2018 Edition from Ubuy in French Polynesia. Ubuy offers a convenient shopping experience with a variety of options for obtaining essential academic resources and literature, making it a reliable choice for students and professionals alike.

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XPF 10284

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Caractéristiques et avantages

  • Comprehensive background in data analysis, probability, random variables, and statistical methods
  • Includes topics like machine learning, simulation, classification, regression, and clustering
  • Illustrated throughout with worked examples and pedagogical elements
  • Problems and Programming Exercises at the end of each chapter
  • Instructor resources available with model solutions and presentation slides

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