عنوان

Probability and statistics for computer science /

شابک

3319644092

شابک

3319644106

شابک

9783319644097

شابک

9783319644103

شابک اشتباه

9783319644097

کد کشور

bnb

شماره

b624063

عنوان اصلي

Probability and statistics for computer science /

نام عام مواد

[Book]

نام نخستين پديدآور

David Forsyth.

نام خاص و کميت اثر

1 online resource (xxiv, 367 pages) :

ساير جزييات

illustrations (some color)

متن يادداشت

Includes index.

متن يادداشت

1 Notation and conventions -- 2 First Tools for Looking at Data -- 3 Looking at Relationships -- 4 Basic ideas in probability -- 5 Random Variables and Expectations -- 6 Useful Probability Distributions -- 7 Samples and Populations -- 8 The Significance of Evidence -- 9 Experiments -- 10 Inferring Probability Models from Data -- 11 Extracting Important Relationships in High Dimensions -- 12 Learning to Classify -- 13 Clustering: Models of High Dimensional Data -- 14 Regression -- 15 Markov Chains and Hidden Markov Models -- 16 Resources.

بدون عنوان

0

متن يادداشت

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." A chapter 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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix. 9783319644103

شماره استاندارد بين المللي کتاب و موسيقي

9783319644097

موضوع مستند نشده

Computer science-- Statistical methods.

موضوع مستند نشده

3D graphics & modelling.

موضوع مستند نشده

Computer science-- Statistical methods.

موضوع مستند نشده

Computers-- Computer Simulation.

موضوع مستند نشده

Computers-- Mathematical & Statistical Software.

موضوع مستند نشده

Mathematical & statistical software.

موضوع مستند نشده

Maths for computer scientists.

موضوع مستند نشده

COM077000

موضوع مستند نشده

UFM

موضوع مستند نشده

UFM

موضوع مستند نشده

UYAM

موضوع مستند نشده

UYAM

شماره

ويراست

شماره رده

مستند نام اشخاص تاييد نشده

Forsyth, David

تاريخ عمليات

20201221083214.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

کد کاربرگه

270410

تكميل شده

Y