Introduction to Machine Learning

Introduction to Machine Learning

These lecture notes are used in an introductory course in Machine Learning at Purdue University. Strong background in Probability theory, Linear Algebra and Programming are a must.

Publication date: 01 Oct 2010

ISBN-10: n/a

ISBN-13: n/a

Paperback: 234 pages

Views: 2,538

Type: N/A

Publisher: Cambridge University Press

License: n/a

Post time: 02 Apr 2016 11:00:00

Introduction to Machine Learning

Introduction to Machine Learning These lecture notes are used in an introductory course in Machine Learning at Purdue University. Strong background in Probability theory, Linear Algebra and Programming are a must.
Tag(s): Machine Learning Statistics
Publication date: 01 Oct 2010
ISBN-10: n/a
ISBN-13: n/a
Paperback: 234 pages
Views: 2,538
Document Type: N/A
Publisher: Cambridge University Press
License: n/a
Post time: 02 Apr 2016 11:00:00
From the Course Description:
Alexander J. Smola wrote:With the availability of cheap storage devices our ability to collect and store large amounts of data is increasing exponentially. Machine learning is a branch of applied statistics which aims to bring to bear tools from statistics in the analysis of such large datasets. This course is a biased journey through some of dominant concepts in machine learning. This is an INTRODUCTORY course in Machine Learning. As such, it will cover basic concepts from both computer science as well as statistics. In first part of the course we will review linear algebra, probability theory, and programming at a very brisk pace. In the next 3 - 4 weeks we will work on some basic machine learning algorithms such as k-means, k-nearest neighbors, Perceptron etc. Finally, we will switch gears and cover a number of more advanced topics. Students will have a chance to implement and test a machine learning algorithm of their choice as part of a medium-scale programming project.

Prerequisites:

Strong background in Probability theory, Linear Algebra and Programming are a must.




About The Author(s)


Alexander J. Smola is a Professor in Machine Learning Department at Carnegie Mellon University and CEO of Marianas Labs. His primary research interest covers scalability of algorithms, kernels methods, statistical modeling, and applications.

Alexander J. Smola

Alexander J. Smola is a Professor in Machine Learning Department at Carnegie Mellon University and CEO of Marianas Labs. His primary research interest covers scalability of algorithms, kernels methods, statistical modeling, and applications.


SVN Vishwanathan is a Professor of Computer Science at the University of California, Santa Cruz. He received his Ph.D in machine learning from the Department of Computer Science and AutomationIndian Institute of Science in 2003. His research goal is to design, analyze, and implement novel machine learning algorithms that take advantage of modern hardware to enable learning on and mining of massive datasets. 

S V N Vishwanathan

SVN Vishwanathan is a Professor of Computer Science at the University of California, Santa Cruz. He received his Ph.D in machine learning from the Department of Computer Science and AutomationIndian Institute of Science in 2003. His research goal is to design, analyze, and implement novel machine learning algorithms that take advantage of modern hardware to enable learning on and mining of massive datasets. 


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