Foundations of Data Science for Engineering Problem Solving - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Data Science for Engineering Problem Solving write by Parikshit Narendra Mahalle. This book was released on 2021-08-21. Foundations of Data Science for Engineering Problem Solving available in PDF, EPUB and Kindle. This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.
Foundations of Data Science
Foundations of Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Data Science write by Avrim Blum. This book was released on 2020-01-23. Foundations of Data Science available in PDF, EPUB and Kindle. This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Statistical Foundations of Data Science
Statistical Foundations of Data Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Foundations of Data Science write by Jianqing Fan. This book was released on 2020-09-21. Statistical Foundations of Data Science available in PDF, EPUB and Kindle. Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Foundations of Mathematical Modelling for Engineering Problem Solving
Foundations of Mathematical Modelling for Engineering Problem Solving - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Foundations of Mathematical Modelling for Engineering Problem Solving write by Parikshit Narendra Mahalle. This book was released on 2023-01-10. Foundations of Mathematical Modelling for Engineering Problem Solving available in PDF, EPUB and Kindle. This book aims at improving the mathematical modelling skills of users by enhancing the ability to understand, connect, apply and use the mathematical concepts to the problem at hand. This book provides the readers with an in-depth knowledge of the various categories/classes of research problems that professionals, researchers and students might encounter following which the applications of appropriate mathematical models is explained with the help of case studies. The book is targeted at academicians, researchers, students and professionals who belong to all engineering disciplines.
Data Science for Undergraduates
Data Science for Undergraduates - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data Science for Undergraduates write by National Academies of Sciences, Engineering, and Medicine. This book was released on 2018-11-11. Data Science for Undergraduates available in PDF, EPUB and Kindle. Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.
