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CS92PROD
Proseminar: Machine Learning Methods for Text, Audio and Video Analysis

QAC 239
Spring 2020 not offered
This course may be repeated for credit.
Crosslisting: CIS 239
Certificates: Applied Data Science

In this course, students will learn machine learning techniques to analyze text, audio, and video data. The course consists of three parts: text analysis, audio analysis and video analysis. Each part will first introduces how these non-traditional data can be converted into mathematical objects suitable for computer processing and, particularly, for the application of machine learning techniques. Then students will learn a selection of supervised and unsupervised learning algorithms that are effective for text, audio, image/video analysis. Finally, students will explore major applications of these techniques such as sentiment analysis, speech emotion recognition, face recognition, pedestrian detection, keyframe extraction.
Credit: 1 Gen Ed Area Dept: NSM QAC
Course Format: Laboratory CourseGrading Mode: Student Option
Level: UGRD Prerequisites: COMP112 OR QAC155 OR QAC156
Fulfills a Major Requirement for: (CADS)(DATA-MN)

Last Updated on OCT-14-2019
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