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Viewing: DATS 6203 : Machine Learning II: Data Analysis

Last approved: Mon, 16 May 2016 08:51:15 GMT

Last edit: Mon, 09 May 2016 15:42:40 GMT

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Columbian College of Arts and Sciences
Data Science (DATS)
Machine Learning II: Data Analysis
Machine Learning II
Fall 2016
Course Type
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
PHYS 6720 - Biophysics II
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 6720 - Biophysics II
Fee Type


This course is a practical approach to fundamentals of machine learning with an emphasis on data analysis; i.e., how to extract useful information from different datasets Topics include linear models, error and noise, training and testing methods, and generalization as used in machine learning.
Students should be able to explain fundamental ideas and concepts of data analysis methods, demonstrate the knowledge of where the methods can be applied to problems in machine learning, and be able to use data learning techniques to solve typical problems addressed in data science.
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
Course Attribute

Key: 9856