Data Science and Machine Learning: Mathematical and Statistical Methods
- ISBN-10
- 1000730778
- ISBN-13
- 9781000730777
- Category
- Business & Economics
- Pages
- 510
- Language
- English
- Published
- 2019-11-20
- Publisher
- CRC Press
- Authors
- Dirk P. Kroese, Thomas Taimre, Zdravko Botev
Description
"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" -Nicholas Hoell, University of Toronto "This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: Focuses on mathematical understanding. Presentation is self-contained, accessible, and comprehensive. Extensive list of exercises and worked-out examples. Many concrete algorithms with Python code. Full color throughout. The Authors: Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method—an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences. Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley). Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.
Get the book
Similar books
-
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R
By Daniel D. GutierrezThis book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.
-
Machine Learning and Data Science: Fundamentals and Applications
By Nisheeth Joshi, Charu Gupta, Anand SharmaMACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, ...
-
Data Scientist Pocket Guide: Over 600 Concepts, Terminologies, and Processes of Machine Learning and Deep Learning Assembled Together (English Edition)
By Mohamed SabriDESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics.
-
Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies
By Patrick BangertViewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.
-
Hands-On Data Science and Python Machine Learning
By Frank KaneThis book covers the fundamentals of machine learning with Python in a concise and dynamic manner.
-
Machine Learning and Data Science in the Power Generation Industry: Best Practices, Tools, and Case Studies
By Patrick Bangert... machine learning approach based on response time and reliability for islanding detection of distributed generation. IET Renew. Power Gener. 11 (11), 1392–1400. Aldrich, C., Auret, L., 2013. Unsupervised Process Monitoring and Fault ...
-
Machine Learning and Data Science Blueprints for Finance
By Hariom Tatsat, Sahil Puri, Brad LookabaughThis book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ...
-
Machine Learning: A Probabilistic Perspective
By Kevin P. MurphyIn UIAL Meila , M. and T. Jaakkola ( 2006 ) . Mitchell , T. ( 1997 ) . Machine Learning . Tractable Bayesian learning of tree Minka , T. ( 1999 ) . Pathologies of ortho McGraw Hill . belief networks . Statistics and Com dox statisies .
-
Introducing Data Science: Big data, machine learning, and more, using Python tools
By Davy Cielen, Arno Meysmancorrecting errors early Cortez, P. countPerMed variable Coursera CPU compressed data CPU starvation create statement, Cypher createNum() function CreateTable() function, 2nd Crossfilter.js library overview setting up dc.js application ...
-
Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence
By Yoni Nazarathy, Hayden KlokDr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom.