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Topics in AI: Introduction to Machine Learning with Support Vector Machines

Spring 2018


Dr. Lutz Hamel
Tyler, Rm 251
Office Hours: Tuesday 2-3pm and Thursday 11am-noon


Support vector machines (SVMs) belong to a new class of machine learning algorithms with their origins firmly rooted in statistical learning theory. Due to the strong theoretical foundation these algorithms possess desirable properties such as the ability to learn from very small sample sets and a firm estimation of the generalization capacity of the learned model. These properties make this new class of learning algorithms extremely attractive to the practitioner who is frequently faced with "not enough data" and needs to understand "how good a constructed model" actually is. The fact that SVMs have surpassed the performance of artificial neural networks in many areas such as text categorization, speech recognition and bioinformatics bears witness to the power of this class of learning algorithms.

This course is an introduction to machine learning and SVMs. We begin by framing the notion of machine learning and then develop basic concepts such as hyperplanes, features spaces and kernels necessary for the construction of SVMs. Once the theoretical groundwork has been laid we look at practical examples where this class of algorithms can been applied. Here we use machine learning as a knowledge discovery tool. We will use the statistical computing environment R for our experiments.

The goals of this course are for you,


All announcements other than code snippets are made through Sakai
[1/22/18] Welcome!

Documents of Interest:

Data Sets:

Many of the packages above have accompanying data sets.  But the premier source for experimental machine learning data sets is the
UCI  Machine Learning Repository.