Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had significant impact on both algorithms and applications.
Pattern Recognition and Machine Learning
The book coveris probabilistic approaches to machine learning, including Bayesian networks, graphical models, kernel methods, and EM algorithms. It emphasizes a statistical perspective over purely algorithmic approaches, helping formalize machine learning as a probabilistic inference problem. Its clear mathematical treatment and broad coverage have made it a standard reference for researchers and graduate students. The book’s impact lies in shaping the modern probabilistic framework widely used in fields like computer vision, speech recognition, and bioinformatics, deeply influencing the development of Bayesian machine learning methods.
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- Websitewww.microsoft.com
- AuthorsChristopher M. Bishop
- Published date2006/08/17
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MLSysBook (Machine Learning Systems) is an open, community-driven textbook and learning stack for AI systems engineering led by the Harvard EDGE / MLSysBook community. The repository houses the textbook source, TinyTorch (a small educational DL framework), hardware lab kits, and supporting materials to teach how to design, build, benchmark, and deploy real-world machine learning systems.
