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Author: Jeff M. Phillips
Publisher: Springer Nature
ISBN: 3030623416
Category : Mathematics
Languages : en
Pages : 299
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Book Description
This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
Author: Jeff M. Phillips
Publisher: Springer Nature
ISBN: 3030623416
Category : Mathematics
Languages : en
Pages : 299
Get Book Here
Book Description
This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
Author: John Mandel
Publisher: Courier Corporation
ISBN: 048613959X
Category : Mathematics
Languages : en
Pages : 434
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Book Description
First half of book presents fundamental mathematical definitions, concepts, and facts while remaining half deals with statistics primarily as an interpretive tool. Well-written text, numerous worked examples with step-by-step presentation. Includes 116 tables.
Author: S.C. Gupta
Publisher: Sultan Chand & Sons
ISBN: 9351611736
Category : Mathematics
Languages : en
Pages : 22
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Book Description
Knowledge updating is a never-ending process and so should be the revision of an effective textbook. The book originally written fifty years ago has, during the intervening period, been revised and reprinted several times. The authors have, however, been thinking, for the last few years that the book needed not only a thorough revision but rather a substantial rewriting. They now take great pleasure in presenting to the readers the twelfth, thoroughly revised and enlarged, Golden Jubilee edition of the book. The subject-matter in the entire book has been re-written in the light of numerous criticisms and suggestions received from the users of the earlier editions in India and abroad. The basis of this revision has been the emergence of new literature on the subject, the constructive feedback from students and teaching fraternity, as well as those changes that have been made in the syllabi and/or the pattern of examination papers of numerous universities. Knowledge updating is a never-ending process and so should be the revision of an effective textbook. The book originally written fifty years ago has, during the intervening period, been revised and reprinted several times. The authors have, however, been thinking, for the last few years that the book needed not only a thorough revision but rather a substantial rewriting. They now take great pleasure in presenting to the readers the twelfth, thoroughly revised and enlarged, Golden Jubilee edition of the book. The subject-matter in the entire book has been re-written in the light of numerous criticisms and suggestions received from the users of the earlier editions in India and abroad. The basis of this revision has been the emergence of new literature on the subject, the constructive feedback from students and teaching fraternity, as well as those changes that have been made in the syllabi and/or the pattern of examination papers of numerous universities. Knowledge updating is a never-ending process and so should be the revision of an effective textbook. The book originally written fifty years ago has, during the intervening period, been revised and reprinted several times. The authors have, however, been thinking, for the last few years that the book needed not only a thorough revision but rather a substantial rewriting. They now take great pleasure in presenting to the readers the twelfth, thoroughly revised and enlarged, Golden Jubilee edition of the book. The subject-matter in the entire book has been re-written in the light of numerous criticisms and suggestions received from the users of the earlier editions in India and abroad. The basis of this revision has been the emergence of new literature on the subject, the constructive feedback from students and teaching fraternity, as well as those changes that have been made in the syllabi and/or the pattern of examination papers of numerous universities. Some prominent additions are given below: 1. Variance of Degenerate Random Variable 2. Approximate Expression for Expectation and Variance 3. Lyapounov’s Inequality 4. Holder’s Inequality 5. Minkowski’s Inequality 6. Double Expectation Rule or Double-E Rule and many others
Author: Norman Matloff
Publisher: CRC Press
ISBN: 0429687117
Category : Business & Economics
Languages : en
Pages : 289
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Book Description
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Author: Palle E. T. Jorgensen
Publisher: Springer Science & Business Media
ISBN: 0387330828
Category : Mathematics
Languages : en
Pages : 320
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Book Description
Combines analysis and tools from probability, harmonic analysis, operator theory, and engineering (signal/image processing) Interdisciplinary focus with hands-on approach, generous motivation and new pedagogical techniques Numerous exercises reinforce fundamental concepts and hone computational skills Separate sections explain engineering terms to mathematicians and operator theory to engineers Fills a gap in the literature
Author: Joseph K. Blitzstein
Publisher: CRC Press
ISBN: 1466575573
Category : Mathematics
Languages : en
Pages : 599
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Book Description
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
Author: Peggy Warren
Publisher: Quickstudy Reference Guides
ISBN: 9781423247272
Category :
Languages : en
Pages : 6
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Book Description
Essential core elements of mathematics to support early learning, continued development, and as a reference to review during and after building a strong foundation. Seeing a broad overview and how the details make the math possible in just 6 pages can melt away some math phobia and will strengthen skills and grades. Written and tested in a classroom over many years, two teachers came to us with this reference they used with their students. A math textbook authoring group then expanded the series. With experts in the classroom and in textbooks developing the content, don't pass up this 6 page laminated, inexpensive tool with the power to support the core areas of math students struggle with. Check other titles in the 5-guide series for the areas of support most needed. 6 page laminated guide includes: Ways to Collect Data Populations & Samples Types of Samples Random Sample Convenience Sample Systematic Sample Cluster Sample Stratified Sample Volunteer Sample Measures of Central Tendency Measures of Spread Range Interquartile Range (IQR) When to use Measures of Center & Spread Symmetric Distribution (also called Normal Distribution) Negatively Skewed Distribution (also called Left Skewed Distribution) Positively Skewed Distribution (also called Right Skewed Distribution) Interpretation of Graphs Ways to Prevent Misinterpretation of Graphs Graphing Data Checklist for Making Graphs Data Displays Pictograph, Frequency Table, Tally Marks Bar Graph, Double & Multiple Bar Graphs Stacked Bar Graph Two-Way Frequency & Relative Frequency Table Circle Graph (also called Pie Chart) & Steps to Make Circle Graph Scatter Plot & Steps to Make a Scatter Plot Box-and-Whisker Plot & Steps to Make a Box-and-Whisker Plot Histogram & Steps to Make a Histogram Distributions in Histograms Line Graph & Line Plot Stem-and-Leaf Plot Multiple Line Graph Double Stem-and-Leaf Plot Interpreting Statistics Interpolation / Extrapolation Normal Distribution Venn Diagram To determine if a set of data has any outliers: Matrix Disjoint Sets Interpreting the Venn Diagram Probability
Author: Larry Wasserman
Publisher: Springer Science & Business Media
ISBN: 0387217363
Category : Mathematics
Languages : en
Pages : 446
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Book Description
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author:
Publisher: Houghton Mifflin Harcourt
ISBN: 0544180941
Category :
Languages : en
Pages : 603
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Book Description
