Proseminar: Machine Learning Methods for Audio and Video Analysis
QAC 239
Spring 2024
| Section:
01
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| This course may be repeated for credit. |
| Crosslisting:
CIS 239 |
| Course Cluster and Certificates: Applied Data Science Certificate |
In this course, students are introduced to machine learning techniques to analyze image, audio, and video data. The course is organized in three parts, and in each part we will first introduce how these nontraditional data can be converted into appropriate (mathematical) objects suitable for computer processing, and, particularly, for the application of machine learning techniques. Students then will learn and work with a number of machine learning algorithms and deep learning methods that are effective for image and audio analysis. We will also explore major applications of these techniques such as object detection, face recognition, image classification, audio classification, speaker detection, and speech recognition. |
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| Credit: 1 |
Gen Ed Area Dept:
NSM QAC |
| Course Format: Laboratory Course | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: COMP112 OR QAC155 OR QAC156 |
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Fulfills a Requirement for: (CADS)(DATA-MN)(PSYC) |
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Past Enrollment Probability: 75% - 89% |
| SECTION 01 | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
Various journal articles
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Examinations and Assignments:
Course requirements include several assignments, a term project and presentation |
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 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. Professor Kaparakis will be handling pre-requisite override requests during pre-registration, but is not the instructor for this course.
PLEASE NOTE: Only graded courses can satisfy the requirements for the data analysis minor and the applied data science certificate. Courses completed with a CR/U grading mode will not satisfy the requirements of the two programs. |
| Instructor(s): Zhang,Meiqing Cakmak,Furkan Times: ..T.R.. 01:20PM-02:40PM; Location: ALLB204; |
| Total Enrollment Limit: 16 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 1 | GRAD: X | SR non-major: 6 | JR non-major: 7 | SO: 3 | FR: 0 |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 3 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 3 |
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