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Deep Learning 000 |
Enrollment Information (not real time - data refreshed nightly)
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Class #:
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20247 | |
Enrollment Capacity:
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150 |
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Section:
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000 |
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Enrollment Total:
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48 |
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Credits:
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3.00 credits
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Seats Available:
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102 |
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Dates:
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08/31/2020 - 12/11/2020 |
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Status:
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OPEN WITH RESERVES |
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Days, Time:
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T , 5:30 PM - 8:10 PM |
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Remote |
view map |
| Location: |
Remote |
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| Reserve Capacities |
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| Description |
Enrollment Capacity |
Enrollment Total |
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| CSE: Seats Reserved |
120 |
37 |
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| Eng Sci MS: Robotics Seats Rsv |
20 |
2 |
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| Eng Sci MS: AI Seats Reserved |
10 |
3 |
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| Enrollment Requirements |
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Prerequisites: Pre-Requisite: CSE 474 or CSE 574. |
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| Course Description |
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| Deep Learning algorithms learn multi-level representations of data, with each level explaining the data in a hierarchical manner. Such algorithms have been effective at uncovering underlying structure in data, e.g., features to discriminate between classes. They have been successful in many artificial intelligence problems including image classification, speech recognition and natural language processing. The course, which will be taught through lectures and projects, will cover the underlying theory, the range of applications to which it has been applied, and learning from very large data sets. The course will cover connectionist architectures commonly associated with deep learning, e.g., basic neural networks, convolutional neural networks, and recurrent neural networks. |
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| Instructor(s) |
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Srihari, S N |
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| On-line Resources |
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| Other Courses Taught By: Srihari, S N |
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