Springer Pattern Recognition and Machine Learning
Product ID: 261744461
📊Bayesian inference algorithms
📚Self-contained probability intro
🧩Graphical models for ML
Fr 429
1
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Description
📈 Unlock the Bayesian edge in machine learning — where math meets mastery!
- ACCESSIBLE YET RIGOROUS - No prior ML knowledge needed—just basic calculus and linear algebra to unlock advanced concepts.
- EXERCISES THAT CEMENT MASTERY - Engage with challenging, well-crafted problems that transform theory into expertise.
- GRAPHICAL MODELS AS A CORE TOOL - Explore machine learning through intuitive graphical models, a unique approach not found in other texts.
- ACCELERATED APPROXIMATE INFERENCE - Leverage fast algorithms that provide practical solutions when exact answers are out of reach.
- MASTER BAYESIAN PATTERN RECOGNITION - Dive deep into the first textbook to fully embrace Bayesian methods in pattern recognition.
Springer's 'Pattern Recognition and Machine Learning' is a pioneering textbook that introduces Bayesian perspectives and graphical models to machine learning. Designed for professionals with a solid math foundation, it offers fast approximate inference algorithms and a self-contained introduction to probability. Highly rated and widely respected, this book is essential for those aiming to deeply understand and implement advanced ML methods beyond surface-level knowledge.
Specifications
| Best Sellers Rank | #154,463 in Books ( See Top 100 in Books ) #357 in Applied Mathematics #417 in Software Design, Testing & Engineering #1,085 in Computer Science |
| Customer reviews | 4.6 4.6 out of 5 stars (746) |
| Dimensions | 19.56 x 3.3 x 25.91 cm |
| Edition | 1st ed. 2006. Corr. 2nd printing 2011 |
| ISBN-10 | 0387310738 |
| ISBN-13 | 978-0387310732 |
| Item weight | 1.05 Kilograms |
| Language | English |
| Print length | 778 pages |
| Publication date | 6 April 2011 |
| Publisher | Springer-Verlag New York Inc. |
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