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Item #:
254230054
Mathematics for Machine Learning, (Hardcover)
Item #:
254230054
€ 107
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This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
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Product Details
- 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.
| Book format | Hardcover |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | April, 2020 |
| Pages | 390 |
| Reading level | General |
| Subgenre | Artificial Intelligence |
| Series title | No Series |
| Edition | 1 |
| Publisher | Cambridge University Press |
| Original languages | English |
| Language | English |
| Edu focus | Math & Counting |
| Educational level | College |
| Is collectible | N |
| Binding type | Case Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.40 x 0.80 x 8.90 in (16.3 x 2 x 22.6 cm) |
| Assembled product weight | 2.1 lb (950 grams) |
| Bisac subject heading | Computers |
Product Description
Mathematics for Machine Learning, (Hardcover)
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Customer Questions & Answers
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Question:
What mathematical topics are covered in this textbook?
Answer: The textbook covers linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. -
Question:
Is this book suitable for beginners in mathematics?
Answer: Yes, it introduces concepts with a minimum of prerequisites, making it accessible for those new to mathematics. -
Question:
Are there additional resources available for learners?
Answer: Yes, the book offers programming tutorials on its website to complement the content.
Marc Peter Deisenroth All Books Editorial Review
Mathematics for Machine Learning is a hardcover non-fiction book published by Cambridge University Press in April 2020. Spanning 390 pages, it covers essential mathematical concepts necessary for understanding machine learning, making it ideal for college-level readers. This book is categorized within the Computing & Internet genre and specifically addresses topics in Artificial Intelligence, which is crucial for anyone looking to dive deeper into the field of machine learning. Its informative content has been positively received by readers seeking to strengthen their math foundation for advanced computing studies.
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Pros
- Comprehensive coverage of necessary mathematical concepts
- Designed for college-level understanding
- High-quality hardcover format
- Focuses on Artificial Intelligence applications
- Useful for both beginners and advanced learners
Cons
- Some readers may find it dense in parts
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€ 107
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Features & Benefits
- Comprehensive introduction to essential mathematical tools for machine learning.
- Covers linear algebra, probability, statistics, and more in a unified approach.
- Bridges the gap between disparate courses for efficient learning.
- Includes derivations of key machine learning methods for practical application.
- Every chapter offers worked examples and exercises to enhance understanding.
- Accompanying programming tutorials available on the book's website.
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