Algorithmic Aspects Of Machine Learning


Algorithmic Aspects Of Machine Learning
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Algorithmic Aspects Of Machine Learning


Algorithmic Aspects Of Machine Learning
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Author : Ankur Moitra
language : en
Publisher: Cambridge University Press
Release Date : 2018-09-27

Algorithmic Aspects Of Machine Learning written by Ankur Moitra and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-27 with Computers categories.


Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.



Algorithmic Aspects Of Cloud Computing


Algorithmic Aspects Of Cloud Computing
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Author : Timos Sellis
language : en
Publisher: Springer
Release Date : 2017-04-07

Algorithmic Aspects Of Cloud Computing written by Timos Sellis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-07 with Computers categories.


This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2016, held in Aarhus, Denmark, in August 2016. The 11 revised full papers presented together with one tutorial paper were carefully reviewed and selected from 30 initial submissions. They deal with the following topics: algorithmic aspects of elasticity and scalability for distributed, large-scale data stores (e.g. NoSQL and columnar databases); search and retrieval algorithms for cloud infrastructures; monitoring and analysis of elasticity for virtualized environments; NoSQL, schemaless data modeling, integration; caching and load-balancing; storage structures and indexing for cloud databases; new algorithmic aspects of parallel and distributed computing for cloud applications; scalable machine learning, analytics and data science; high availability, reliability, failover; transactional models and algorithms for cloud databases; query languages and processing programming models; consistency, replication and partitioning CAP, data structures and algorithms for eventually consistent stores.



Algorithmic Aspects In Information And Management


Algorithmic Aspects In Information And Management
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Author : Weili Wu
language : en
Publisher: Springer Nature
Release Date : 2021-12-16

Algorithmic Aspects In Information And Management written by Weili Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-16 with Computers categories.


This book constitutes the proceedings of the 15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021, which was held online during December 20-22, 2021. The conference was originally planned to take place in Dallas, Texas, USA, but changed to a virtual event due to the COVID-19 pandemic. The 38 regular papers included in this book were carefully reviewed and selected from 62 submissions. They were organized in the following topical sections: approximation algorithms; scheduling; nonlinear combinatorial optimization; network problems; blockchain, logic, complexity and reliability; and miscellaneous.



Understanding Machine Learning


Understanding Machine Learning
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Author : Shai Shalev-Shwartz
language : en
Publisher: Cambridge University Press
Release Date : 2014-05-19

Understanding Machine Learning written by Shai Shalev-Shwartz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-19 with Computers categories.


Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.



Data Science Algorithms In A Week


Data Science Algorithms In A Week
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Author : Dávid Natingga
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31

Data Science Algorithms In A Week written by Dávid Natingga and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.


Build a strong foundation of machine learning algorithms in 7 days Key FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set



Algorithmic Aspects Of Cloud Computing


Algorithmic Aspects Of Cloud Computing
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Author : Ioannis Chatzigiannakis
language : en
Publisher: Springer Nature
Release Date : 2023-12-13

Algorithmic Aspects Of Cloud Computing written by Ioannis Chatzigiannakis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-13 with Computers categories.


This book constitutes revised selected papers from the 8th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2023, held in Amsterdam, The Netherlands, on September 5, 2023. The 13 full papers included in this book were carefully reviewed and selected from 24 submissions. They focus on algorithmic aspects of computing and data management in modern cloud-based systems interpreted broadly so as to include edge- and fog-based systems, cloudlets, cloud micro-services, virtualization environments, decentralized systems, as well as dynamic networks.



Machine Learning With R


Machine Learning With R
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Author : Abhijit Ghatak
language : en
Publisher:
Release Date : 2017

Machine Learning With R written by Abhijit Ghatak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Machine learning categories.


This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.



Data Science Algorithms In A Week Second Edition


Data Science Algorithms In A Week Second Edition
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Author : David Natingga
language : en
Publisher:
Release Date : 2018-10-31

Data Science Algorithms In A Week Second Edition written by David Natingga and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.


Build a strong foundation of machine learning algorithms in 7 days Key Features Use Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a week Know when and where to apply data science algorithms using this guide Book Description Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem What you will learn Understand how to identify a data science problem correctly Implement well-known machine learning algorithms efficiently using Python Classify your datasets using Naive Bayes, decision trees, and random forest with accuracy Devise an appropriate prediction solution using regression Work with time series data to identify relevant data events and trends Cluster your data using the k-means algorithm Who this book is for This book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You'll also find this book useful if you're currently working with data science algorithms in some capacity and want to expand your skill set



Machine Learning


Machine Learning
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Author : Mohssen Mohammed
language : en
Publisher: CRC Press
Release Date : 2016-08-19

Machine Learning written by Mohssen Mohammed and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-19 with Computers categories.


Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.



Machine Learning And Big Data


Machine Learning And Big Data
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Author : Uma N. Dulhare
language : en
Publisher: John Wiley & Sons
Release Date : 2020-09-01

Machine Learning And Big Data written by Uma N. Dulhare and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-01 with Computers categories.


This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.