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| Machine Learning Refined Foundations, Algorithms, and Applications |
A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
- Encourages geometric intuition and algorithmic thinking to provide an intuitive understanding of key concepts and an interactive way of learning
- Features coding exercises for Python to help put knowledge into practice
- Emphasizes practical applications, with real-world examples, to give students the confidence to conduct research, build products, and solve problems
- Completely self-contained, with appendices covering the essential mathematical prerequisites
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Understanding Machine Learning - From Theory to Algorithms
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| Understanding Machine Learning From Theory to Algorithms |
Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
- Provides a principled development of the most important machine learning tools
- Describes a wide range of state-of-the-art algorithms
- Promotes understanding of when machine learning is relevant, what the prerequisites for a successful application of ML algorithms are, and which algorithms to use for any given task
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Mathematics for Machine Learning
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| Mathematics for Machine Learning |
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.
- A one-stop presentation of all the mathematical background needed for machine learning
- Worked examples make it easier to understand the theory and build both practical experience and intuition
- Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines
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Linear Algebra and Learning from Data
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| Linear Algebra and Learning from Data |
- The first textbook designed to teach linear algebra as a tool for deep learning
- From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra
- Includes the necessary background from statistics and optimization
- Explains stochastic gradient descent, the key algorithim of deep learning, in detail
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📚 Enjoy your reading day!
Source: Cambridge University Press
