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Item #:
105577288
Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Item #:
105577288
LYD 906
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This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques.
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Product Details
- Well-rounded, rigorous, and informative work on the mathematics behind modern machine learning techniques.
- Ideal for a course on data science for mathematics students at an advanced undergraduate or early graduate level.
- Fills a gap in the existing literature by presenting proofs of major theorems and providing a copious amount of Python code.
- Provides a deeper dive into data-scientific methods compared to many introductory texts.
- Focuses on mathematical understanding, with a self-contained and accessible presentation.
- Includes extensive list of exercises and worked-out examples, concrete algorithms with Python code, and full-color illustrations.
| Publisher | Chapman and Hall/CRC |
| Publication date | 22 Nov. 2019 |
| Edition | 1st |
| Language | English |
| Print length | 538 pages |
| ISBN-10 | 1138492531 |
| ISBN-13 | 978-1138492530 |
| Item weight | 1.69 kg |
| Dimensions | 22.23 x 3.18 x 27.94 cm |
| Part of series | Chapman & Hall/Crc Machine Learning & Pattern Recognition |
Product Description
Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
About This Item
Introducing "Data Science and Machine Learning: Mathematical and Statistical Methods" - the ultimate guide for anyone interested in diving deep into the world of data science and machine learning. Authored by a team of renowned experts, this book provides a comprehensive overview of the fundamental mathematical and statistical methods used in these fields. Written by Dirk P. Kroese, a Professor of Mathematics and Statistics at The University of Queensland, this book offers insights from over 120 articles and five books that cover various areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods.
Dr. Kroese is a pioneer of the Cross-Entropy method, a widely acclaimed adaptive Monte Carlo technique used worldwide to solve complex estimation and optimization problems in science, engineering, and finance. Joining him is Zdravko Botev, an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning, recognized by the Australian Academy of Science with the prestigious Christopher Heyde Medal in 2018 for his distinguished research in mathematical sciences. Thomas Taimre, a Senior Lecturer of Mathematics and Statistics at The University of Queensland, contributes his expertise in applied probability, Monte Carlo methods, applied physics, and the self-mixing effect in lasers.
Lastly, Radislav Vaisman, a Lecturer of Mathematics and Statistics, brings his experience in applied probability, machine learning, and computer science to round out the team. Whether you're a beginner or already have some knowledge in the field, this book caters to all levels of expertise. With topics ranging from foundational concepts to advanced applications, this book covers it all. Gain insights into machine learning algorithms, explore data science techniques, and understand the principles behind pattern recognition.
Enhance your skills and stay up-to-date with the latest trends, best practices, and real-world case studies. Unlock the potential of data science and machine learning with this indispensable resource, available in the first edition from Chapman & HallCRC. Don't miss out on this opportunity to expand your knowledge and advance your career in the exciting field of data science and machine learning. Keywords: Chapman & HallCRC Machine Learning, Chapman & HallCRC Data Science and Machine Learning, Chapman & HallCRC Pattern Recognition 1st Edition, Machine Learning and Data Science, Data Science and Machine Learning, Pattern Recognition 1st Edition, Machine Learning and Data Science books, Data Science and Machine Learning tutorials, Pattern Recognition 1st Edition guide, Machine Learning and Data Science resources, Data Science and Machine Learning techniques, Pattern Recognition 1st Edition techniques, Machine Learning and Data Science applications, Data Science and Machine Learning algorithms, Pattern Recognition 1st Edition examples, Machine Learning and Data Science trends, Data Science and Machine Learning career, Pattern Recognition 1st Edition research, Machine Learning and Data Science tips, Data Science and Machine Learning best practices, Pattern Recognition 1st Edition case studies.
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Question:
Who is the target audience for this textbook?
Answer: Mathematics students at the advanced undergraduate or early graduate level -
Question:
What does this textbook provide?
Answer: Proofs of major theorems and derivations, Python code, and a deeper dive into data-scientific methods -
Question:
Does this textbook focus on mathematical understanding?
Answer: Yes, it focuses on mathematical understanding and includes a comprehensive list of exercises and worked-out examples
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Features & Benefits
- Ideal for mathematics students at the advanced undergraduate or early graduate level
- Provides proofs of major theorems and derivations
- Includes a copious amount of Python code
- Deeper dive into data-scientific methods compared to introductory texts
- Focuses on mathematical understanding
- Extensive list of exercises and worked-out examples
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