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Instructor Class Description

Time Schedule:

James Jeffry Howbert
CSS 490
Bothell Campus

Special Topics in Computing and Software Systems

Examines current topics and issues associated with computing and software systems. Offered: AWSpS.

Class description

Introduction to Machine Learning

Machine learning is the science of building predictive models from available data, in order to predict the behavior of new, previously unseen data. It lies at the intersection of modern statistics and computer science, and is widely and successfully used in medicine, image recognition, finance, e-commerce, textual analysis, and many areas of scientific research, especially computational biology. This course is an introduction to the theory and practical use of the most commonly used machine learning techniques, including decision trees, logistic regression, discriminant analysis, neural networks, naļve Bayes, k-nearest neighbor, support vector machines, collaborative filtering, clustering, and ensembles. The coursework will emphasize hands-on experience applying specific techniques to real-world datasets, combined with several programming projects.

This course is open to both undergraduate and graduate students and both groups are encouraged to enroll. There will be additional assignments and project deliverables for graduate students.

Student learning goals

General method of instruction

Recommended preparation

Prerequisites include CSS 342, calculus, and statistics; some prior exposure to probability and linear algebra is recommended.

Class assignments and grading


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by James Jeffry Howbert
Date: 11/09/2011