MACHINE LEARNING
found 109 books
Just Enough R
by Sivakumaran Raman
If your job involves working with data in any manner, you cannot afford to ignore the R revolution! If your domain is called data analysis, analytics, informatics, data science, reporting, business intelligence, data management, big data, or visualization, you just have to learn R as this programming language is a game-changing sledgehammer. How...
Computer and Information Sciences
by Tadeusz Czachórski, Erol Gelenbe, Krzysztof Grochla, Ricardo Lent
This book constitutes the refereed proceedings of the 31st International Symposium on Computer and Information Sciences, ISCIS 2016, held in Krakow, Poland, in October 2016. The 29 revised full papers presented were carefully reviewed and selected from 65 submissions. The papers are organized in topical sections on smart algorithms; data classific...
Computer Vision Metrics
by Scott Krig
Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, ...
Computer Aided Verification
by Isil Dillig, Serdar Tasiran
The book set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organ...
Theoretical and Practical Advances in Computer-based Educational Measurement
by Bernard Veldkamp, Cor Sluijter
This book presents a multitude of different, yet related, innovations in educational measurement and provides insight in their practical possibilities.The book not only addresses several improvements in the quality of educational measurement and innovations in (inter)national large scale assessments, but also several advances in psychometrics and i...
Fundamentals of Clinical Data Science
by Pieter Kubben, Michel Dumontier, Andre Dekker
This book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using tec...
Language Technologies for the Challenges of the Digital Age
by Georg Rehm, Thierry Declerck
This open access volume constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceeding...
Nanoinformatics
by Isao Tanaka
This book brings out the state of the art on how informatics-based tools are used and expected to be used in nanomaterials research. There has been great progress in the area in which "big-data" generated by experiments or computations are fully utilized to accelerate discovery of new materials, key factors, and design rules. Data-intensi...
Elements of Robotics
by Mordechai Ben-Ari, Francesco Mondada
This book bridges the gap between playing with robots in school and studying robotics at the upper undergraduate and graduate levels to prepare for careers in industry and research. Robotic algorithms are presented formally, but using only mathematics known by high-school and first-year college students, such as calculus, matrices and probability. ...
The Elements of Data Analytic Style
by Jeff Leek
The dramatic change in the price and accessibility of data demands a new focus on data analytic literacy. This book is intended for use by people who perform regular data analyses. It aims to give a brief summary of the key ideas, practices, and pitfalls of modern data analysis. One goal is to summarize in a succinct way the most common difficultie...