A Course in Machine Learning

A Course in Machine Learning

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.).

Publication date: 01 Sep 2015

ISBN-10: n/a

ISBN-13: n/a

Paperback: 193 pages

Views: 3,456

Type: N/A

Publisher: n/a

License: n/a

Post time: 02 Apr 2016 11:00:00

A Course in Machine Learning

A Course in Machine Learning 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.).
Tag(s): Machine Learning
Publication date: 01 Sep 2015
ISBN-10: n/a
ISBN-13: n/a
Paperback: 193 pages
Views: 3,456
Document Type: N/A
Publisher: n/a
License: n/a
Post time: 02 Apr 2016 11:00:00
Terms and Conditions:
Hal Daumé III wrote:You may obtain the written materials by purchasing a ($55) print copy, by downloading the entire book, or by downloading individual chapters below. If you find the electronic version of the book useful and would like to donate a small amount to support further development, that's always appreciated! You can get the source code for the book, labs and other teaching materials onGitHub. The current version is 0.9 (the "beta" pre-release).

From the Introduction:
Hal Daumé III wrote: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.




About The Author(s)


Hal Daumé III is an associate professor in Computer Science and Language Science at UMD where he studies and blogs about natural language processing (ACL, EMNLP) and machine learning (ICML, NIPS). He is currently director of the CLIP lab, and runs the ML reading group.

Hal Daumé III

Hal Daumé III is an associate professor in Computer Science and Language Science at UMD where he studies and blogs about natural language processing (ACL, EMNLP) and machine learning (ICML, NIPS). He is currently director of the CLIP lab, and runs the ML reading group.


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