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Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series),Used
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Webenabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and selfcontained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudocode for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled modelbased approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software packagePMTK (probabilistic modeling toolkit)that is freely available online. The book is suitable for upperlevel undergraduates with an introductorylevel college math background and beginning graduate students.
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- Q: How many pages does this book have? A: This book has one thousand one hundred four pages. It provides a comprehensive overview of machine learning using probabilistic models.
- Q: What is the binding type of this book? A: This book is hardcover. This durable binding type is designed for long-term use and protection.
- Q: What are the dimensions of this book? A: The dimensions are eight point three one inches in length, one point six one inches in width, and nine point three one inches in height. These sizes make it suitable for easy handling.
- Q: Who is the author of this book? A: The author is Kevin P. Murphy. He is well-known in the field of machine learning and probabilistic modeling.
- Q: What topics does this book cover? A: The book covers topics such as probability, optimization, and linear algebra. It also discusses recent developments like deep learning and conditional random fields.
- Q: What is the target audience for this book? A: This book is suitable for upper-level undergraduates and beginning graduate students. A basic background in college-level mathematics is recommended.
- Q: How can I apply the concepts learned in this book? A: You can apply the concepts by implementing algorithms in MATLAB using the provided PMTK software. This toolkit allows practical experimentation with probabilistic models.
- Q: Is this book suitable for beginners in machine learning? A: Yes, it is suitable for beginners with a basic understanding of college mathematics. The book is designed to be accessible and informative.
- Q: Can I use this book for self-study? A: Yes, this book is ideal for self-study. It includes worked examples and illustrations to aid understanding.
- Q: How should I store this book? A: Store the book upright in a cool, dry place. This helps maintain its condition and prevents damage.
- Q: Is it safe to keep this book around children? A: Yes, it is safe, but the content is more suitable for older students. The book covers advanced topics in machine learning.
- Q: Do I need any special tools to read this book? A: No special tools are needed. Just a basic understanding of mathematics and an interest in machine learning will suffice.
- Q: What if my book arrives damaged? A: If your book arrives damaged, contact the seller for a return or exchange. Most retailers have policies in place for damaged items.
- Q: What if I have questions while reading the book? A: You can refer to online forums or study groups focused on machine learning for additional help. Many resources are available for learners.
- Q: Is there a warranty for this book? A: Books generally do not come with warranties. However, check the seller’s return policy for any satisfaction guarantees.