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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

Catalog Pages referencing this course
Programs referencing this course
Columbian College of Arts and Sciences
Data Science (DATS)
DATS
6202
Machine Learning I: Algorithm Analysis
Machine Learning I
Fall 2016
3
Course Type
Lecture
Default Grading Method
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
No
Yes
PHYS 6620 - Biophysics I

Corequisites
DATS 6101 - Introduction to Data Science
An undergraduate degree with a strong background in science, mathematics, or statistics
15
Dr. Chen Zeng
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
Yes
 
PHYS 6620 - Biophysics I
No
Fee Type


No


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