Information Theory, Inference and Learning Algorithms

Price: $72.99 – $52.90
(as of Dec 28,2024 03:26:27 UTC – Details)

Publisher ‏ : ‎ Cambridge University Press; Illustrated edition (September 25, 2003)
Language ‏ : ‎ English
Paperback ‏ : ‎ 642 pages
ISBN-10 ‏ : ‎ 0521642981
ISBN-13 ‏ : ‎ 978-0521642989
Item Weight ‏ : ‎ 3.4 pounds
Dimensions ‏ : ‎ 7 x 1.45 x 10 inches

Information Theory, Inference and Learning Algorithms

In the world of artificial intelligence and machine learning, understanding the principles of information theory, inference, and learning algorithms is crucial for developing successful models and systems. Information theory, first introduced by Claude Shannon in the 1940s, provides a framework for quantifying the amount of information in a signal or data set.

Inference, on the other hand, involves making decisions or predictions based on observed data and prior knowledge. This process is fundamental to machine learning, where algorithms learn from data to make accurate predictions or classifications.

Learning algorithms are the backbone of machine learning systems, as they are responsible for extracting patterns and relationships from data to make predictions or decisions. These algorithms can be supervised, unsupervised, or semi-supervised, depending on the availability of labeled data.

By combining information theory, inference, and learning algorithms, researchers and developers can create powerful machine learning models that can solve complex problems and make accurate predictions. Understanding these concepts is essential for anyone working in the field of artificial intelligence and machine learning.
#Information #Theory #Inference #Learning #Algorithms