Cambridge University Press eBooks· 2012· Book chapter
Bayesian Reasoning and Machine Learning
- 1,656citations
- 2012year
Short summary
This hands-on text introduces machine learning methods to computer science students with modest mathematical backgrounds, covering topics from basic reasoning to advanced techniques within graphical models.
AI-generated from the title and abstract; the full text is not read.
Key points
- Machine learning methods are crucial for extracting value from large datasets across various industries.
- The text targets computer science students with limited backgrounds in linear algebra and calculus.
- It covers basic reasoning to advanced techniques within the framework of graphical models.
- Numerous computer-based and theoretical examples and exercises are included in each chapter.
AI-generated from the title and abstract; the full text is not read.
Abstract
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
The authors' abstract, as published at the source. Cambridge University Press eBooks, 2012 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics