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Check out these books below by Cambridge University Press.
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Small Summaries for Big Data
The massive volume of data generated in modern applications can
overwhelm our ability to conveniently transmit, store, and index it. For
many scenarios, building a compact summary of a dataset that is vastly
smaller enables flexibility and efficiency in a range of queries over
the data, in exchange for some approximation...
Small Summaries for Big Data
Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.
- Examples, figures, and pseudocode enhance understanding of fundamentals and applications
- Written in accessible plain English
- Optional sections of advanced technical material provide further reading for experts without overwhelming novices
Date Published: November 2020
Foundations of Probabilistic Programming
Foundations of Probabilistic Programming
What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs?...
Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.
- Overview of theoretical underpinnings and applications of probabilistic programming
- Comprehensive survey chapters, accessible to graduate students and non-experts
- This title is also available as Open Access on Cambridge Core
Publication planned for: December 2020
125 Problems in Text Algorithms with Solutions
125 Problems in Text Algorithms
with Solutions
String matching is one of the oldest algorithmic techniques, yet still one of the most pervasive in computer science. The past 20 years have seen technological leaps in applications as diverse as information retrieval and compression...
The problems are drawn from a large range of scientific publications, both classic and new. Building up from the basics, the book goes on to showcase problems in combinatorics on words (including Fibonacci or Thue-Morse words), pattern matching (including Knuth-Morris-Pratt and Boyer-Moore like algorithms), efficient text data structures (including suffix trees and suffix arrays), regularities in words (including periods and runs) and text compression (including Huffman, Lempel-Ziv and Burrows-Wheeler based methods).
Publication planned for: February 2021
Mathematical Foundations of Infinite-Dimensional Statistical Models
Mathematical Foundations of
Infinite-Dimensional Statistical Models
In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades...
This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics...
Publication planned for: February 2021
Uncertainty Analysis for Engineers and Scientists A Practical Guide
Uncertainty Analysis for Engineers and Scientists
A Practical Guide
Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Understand how to handle a complete project and how uncertainty enters into various steps...
Whether you are new to the sciences or an experienced engineer, this useful resource provides a practical approach to performing error analysis. Suitable as a text for a junior or senior level laboratory course in aerospace, chemical and mechanical engineering, and for professionals.
- Organizes error analysis into random error, reading error and calibration error, and supplies worksheets for determining them
- Provides a set of linked examples showing how error analysis impacts various aspects of a complex problem
- Gives specific instructions for carrying out error analysis using both Excel and MATLAB®
Publication planned for: January 2021
Practical Philosophy from Kant to Hegel Freedom, Right, and Revolution
Practical Philosophy from Kant to Hegel
Freedom, Right, and Revolution
Scholarship on Kant's practical philosophy has often overlooked its reception in the early days of post-Kantian philosophy and German Idealism. This volume of new essays illuminates that reception and how it informed the development of practical philosophy between Kant and Hegel...
Taken together, the essays provide an historically informed and philosophically nuanced picture of the development of post-Kantian practical philosophy.
- Illuminates the reception of Kant's practical philosophy
- Highlights the importance of lesser-known figures in the German philosophical tradition such as Erhard and Reimarus
- Explores important topics related to right, morality, freedom and revolution
Publication planned for: March 2021
Happy reading 📚books and drink ☕️coffee!
Source: Cambridge University Press.
