Program: MSIS/PhD
Catalog Description
Recently Deep Learning (DL) techniques have shown a lot of promise for tasks in various modalities such as speech, language, and vision and DL has become a go-to machine learning paradigm for Artificial Intelligence (AI) based applications. The course aims to cover theoretical and applied aspects of Deep Learning and how it is used to solve real-world problems. Classes in each week may be divided into two segments: (a) Theory and Methods, a concise description of a deep learning algorithm, and (b) Lab Tutorial, a hands-on session on applying the algorithm on multimodal real world data such as textual, visual and audio data.
Prerequisites
Graduate standing.
Restrictions
Enrollment in Information Studies (INF) courses is restricted to graduate students in the School of Information through registration periods 1 and 2, with outside students only being accepted during period 3.
Current and Upcoming Classes for this Course
| Class Name | Semester | Day(s) | Start Time(s) | End Time(s) | Building | Room |
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| INF 385T: Special Topics in Information Science: Deep Learning and Multimodal Systems
Abhijit Mishra |
Spring Term 2026 |
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Past Classes for this Course
| Class Name | Semester | Day(s) | Start Time(s) | End Time(s) | Building | Room |
|---|---|---|---|---|---|---|
| INF 385T: Special Topics in Information Science: Deep Learning and Multimodal Systems
Abhijit Mishra Syllabus |
Spring Term 2025 |
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| INF 385T: Special Topics in Information Science: Deep Learning and Multimodal Systems
Abhijit Mishra Syllabus |
Fall Term 2023 |
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