Probability and Statistics for Computer Science - Image 1

£42.87

Springer Probability and Statistics for Computer Science

Name: Probability and Statistics for Computer Science
Brand: Springer
Price: 42.87 GBP
Availability: InStock

Computer Science

This is the usual price. Wait for it to drop, or tell us your number.

£43 today · usual range £0–£0 · best ever £37

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 523 days • 523 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 523 days • 7 price levels

Days at Price

Current Price

Price Analysis

Most common price: £39 (214 days, 40.9%)

Price range: £37 - £45

Price levels: 7 different prices over 523 days

Description

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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 3319877887
Category: Books > Subjects > Computing & Internet > Computer Science
Domain: Amazon UK
Release Date: 04 June 2019
Listed Since: 25 January 2019

Barcode

No barcode data available

Springer Probability and Statistics for Computer Science

This is the usual price. Wait for it to drop, or tell us your number.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Applied Machine Learning

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

Machine Learning Algorithms in Depth

Machine Learning: Theory and Practice

An Elementary Introduction to Statistical Learning Theory (Wiley Series in Probability and Statistics)

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

A Mathematical Introduction to Data Science

Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach

Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python

Machine Learning: An Algorithmic Perspective, Second Edition

Data Without Labels: Practical Unsupervised Machine Learning

Model-Based Clustering and Classification for Data Science: With Applications in R: Series Number 50 (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

Statistical Foundations of Data Science (Chapman & Hall/CRC Data Science Series)

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems, 2nd Edition

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Algorithms for Data Science

Model-Based Machine Learning

Machine Learning Foundations: Volume 1: Supervised Learning

An Introduction to Statistical Data Science: Theory and Models

Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020): Emerging Applications and Technologies for Industry ... (Lecture Notes in Networks and Systems, 254)