COMPSCI 271 - Machine Learning

Institution:

Duke University

Subject:

Computer Science

Description:

Theoretical and practical issues in modern machine learning techniques. Topics include statistical foundations, supervised and unsupervised learning, decision trees, hidden Markov models, neural networks, and reinforcement learning. Minimal overlap with Computer Science 270. Prerequisite: Computer Science 100, Mathematics 104, and Statistics 103 or consent of instructor. Instructor: Parr

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

WWW.DUKE.EDU

Phone Number:

(919) 684-8111

Regional Accreditation:

Southern Association of Colleges and Schools

Calendar System:

Semester

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