Call Number | 13167 |
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Day & Time Location |
T 6:10pm-8:00pm 503 Hamilton Hall |
Points | 3 |
Grading Mode | Standard |
Approvals Required | None |
Instructor | Alexander Peterhansl |
Type | SEMINAR |
Method of Instruction | In-Person |
Course Description | Machine learning algorithms continue to advance in their capacity to predict outcomes and rival human judgment in a variety of settings. This course is designed to offer insight into advanced machine learning models, including Deep Learning, Recurrent Neural Networks, Adversarial Neural Networks, Time Series models and others. Students are expected to have familiarity with using Python, the scikit-learn package, and github. The other half of the course will be devoted to students working in key substantive areas, where advanced machine learning will prove helpful -- areas like computer vision and images, text and natural language processing, and tabular data. Students will be tasked to develop team projects in these areas and they will develop a public portfolio of three (or four) meaningful projects. By the end of the course, students will be able to show their work by launching their models in live REST APIs and web-applications. |
Web Site | Vergil |
Department | Quantitative Methods/Social Sciences |
Enrollment | 13 students (61 max) as of 12:05PM Wednesday, December 4, 2024 |
Subject | Quantitative Methods: Social Sciences |
Number | GR5074 |
Section | 001 |
Division | Graduate School of Arts and Sciences |
Note | PRIORITY QMSS STUDENTS |
Section key | 20241QMSS5074G001 |