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A Course in Machine Learning
Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning.
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It's focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
227 pages, Unknown Binding
First published January 1, 2013
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March 1, 2020
My first pass at CIML is the first 10 chapters (decision trees to neural networks), and it does well to fulfill it promises as a very clear introductory text to machine learning. Short chapters, meaningful analogies, without taking anything away from the necessary technicalities. Reading CIML + Introduction to Statistical Learning is a diet every aspiring machine learning scientist must get on quickly.
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