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 |
| 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. |
|
| Credit: 1 |
Gen Ed Area Dept:
NSM QAC |
| Course Format: Laboratory Course | Grading Mode: Student Option |
| Level: UGRD |
Prerequisites: COMP112 OR QAC155 OR QAC156 |
|
Fulfills a Requirement for: (CADS)(DATA-MN)(PSYC) |
Major Readings:
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 |
|
|