Proseminar: Machine Learning Methods for Text, Audio and Video Analysis|
Spring 2020 not offered
|This course may be repeated for credit.|
|Certificates: Applied Data Science|
|Course Cluster: Applied Data Science Certificate|
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.
||Gen Ed Area Dept:
|Course Format: Laboratory Course||Grading Mode: Student Option|
||Prerequisites: COMP112 OR QAC155 OR QAC156
||Fulfills a Major Requirement for: (CADS)(DATA-MN)
Moat, Helen Susannah et al. QUANTIFYING WIKIPEDIA USAGE PATTERNS BEFORE STOCK MARKET MOVES. The University of Warwick. Scientific Reports, Volume 3. Article number 1801. Available online at: http://wrap.warwick.ac.uk/54525/1/WRAP_Moat_srep01801.pdf
Morstatter, Fred and Huan Liu. DISCOVERING, ASSESSING, AND MITIGATING DATA BIAS IN SOCIAL MEDIA. Elsevier preprint. Available online at: http://www.public.asu.edu/~fmorstat/paperpdfs/osnem_preprint.pdf
Munzert, Simon, Christian Rubba, Peter Meissner, and Dominic Nyhuis. AUTOMATED DATA COLLECTION WITH R. A PRACTICAL GUIDE TO WEB SCRAPING AND TEXT MINING. Wiley Publishers, Hoboken, 2014. Available online through Wesleyan library at: https://ebookcentral.proquest.com/lib/wesleyan/detail.action?docID=1824310
|Examinations and Assignments: |
Weekly programming assignments, discussion of methodological literature, and a term project.
|Additional Requirements and/or Comments: |
The course requires a basic programming background that is why COMP 112, QAC155, QAC156 etc. are formal prerequisites. Pre-req overrides will be approved by the Professor for students who satisfy this basic requirement through other course work. The course includes a strong lab component and programming in R and Python is a significant part of the course work.
|Drop/Add Enrollment Requests|
|Total Submitted Requests: 0||1st Ranked: 0||2nd Ranked: 0||3rd Ranked: 0||4th Ranked: 0||Unranked: 0|