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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.

Uploaded a Course Syllabus

Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.

Course Attribute

Key: 9855