Preview Workflow

The CIM Courses system will be down temporarily undergoing routine maintenance.

Viewing: CSCI 4364 : Machine Learning

Last approved: Tue, 07 Mar 2017 09:02:17 GMT

Last edit: Sat, 25 Feb 2017 16:43:20 GMT

Catalog Pages referencing this course
Programs referencing this course
Other Courses referencing this course

In The Catalog Description:

CSCI 6364 : Machine Learning
School of Engineering and Applied Sciences
Computer Science (CSCI)
CSCI
4364
Machine Learning
Machine Learning
Fall 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade

No
No
CSCI 3212, CSCI 3362 and MATH 2184
Corequisites

35

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


No


Overview of core machine learning techniques: nearest-neighbor, regression, classification, perceptron, kernel methods, support vector machine (SVM), logistic regression, ensemble methods, 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: 2015