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Viewing: CSCI 6364 : Machine Learning

Last approved: Thu, 13 Apr 2017 15:57:51 GMT

Last edit: Wed, 22 Mar 2017 23:19:02 GMT

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In The Catalog Prerequisites:

CSCI 6365 : Advanced Machine Learning

In The Catalog Description:

CSCI 4364 : Machine Learning

As A Banner Equivalent:

CSCI 4364 : Machine Learning

As A Banner Prerequisite:

CSCI 6365 : Advanced Machine Learning
School of Engineering and Applied Sciences
Computer Science (CSCI)
CSCI
6364
Machine Learning
Machine Learning
Fall 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade

No
No
CSCI 6212 and CSCI 6362
Corequisites

35

Frequency of Offering
Every Year
Term(s) Offered
Fall
Are there Course Equivalents?
No
 
No
Fee Type


No


Machine learning algorithms: nearest-neighbor, regression, classification, perceptron, kernel methods, support vector machine (SVM), logistic regression, ensemble methods, boosting, graphical models, hidden Markov models (HMM), non-parametrics, online learning, active learning, clustering, feature selection, parameter tuning, and cross-validation. Students cannot receive credit for both CSCI 4364 taken while an undergraduate and CSCI 6364 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 4364 and CSCI 6364.
As a result of taking this course, students should be able to apply machine learning techniques to practical problems. They should also develop an understanding of which techniques are applicable to which problems. Advanced students will also gain tools for the design and analysis of machine learning algorithms.
Uploaded a Course Syllabus

Course Attribute


Key: 2197