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Viewing: DATS 6202 : Machine Learning I: Algorithm Analysis

Last approved: Mon, 16 May 2016 08:48:11 GMT

Last edit: Mon, 09 May 2016 15:01:13 GMT

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Columbian College of Arts and Sciences
Data Science (DATS)
Machine Learning I: Algorithm Analysis
Machine Learning I
Fall 2016
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Letter Grade
Designed primarily for students in the Data Science program, however other students with appropriate backgrounds can register for the course with permission of the instructor
PHYS 6620 - Biophysics I

DATS 6101 - Introduction to Data Science
An undergraduate degree with a strong background in science, mathematics, or statistics
Dr. Chen Zeng
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PHYS 6620 - Biophysics I
Fee Type


This course is a practical approach to fundamentals of algorithm design associated with machine learning. Topics include techniques of statistical and probability theory, combinatorial optimization, and factor graph and graph ensemble as used in machine learning.
Students should be able to explain fundamental ideas and concepts of algorithm design methods, demonstrate the knowledge of where the methods can be applied to problems in machine learning, and be able to use related techniques to solve typical problems addressed in machine learning.
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
Course Attribute

Key: 9855