# Intro To Deep Learning

## ECSE-6850

Deep learning fundamentals and applications in artificial intelligence. Topics include machine learning foundation, linear regression and classification, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversary neural networks, Bayesian neural networks, deep Boltzmann machine, deep Bayesian networks, and deep reinforcement learning.

3 credits
Prereqs:
CSCI-1200 Data Structures
and
ECSE-2500 Engineering Probability
and
one of:
MATH-2010 Multivar Calc & Matrix Algebra MATH-2011 Multivariable Calculus
and
MATH-2012 Matrix Algebra

# Past Term Data
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Offered as Cross-Listing Only
No Term Data
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| Spring | Summer | Fall
--- | --- | --- | ---
| (Session 1) | (Session 2) |
2025 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 0/30 |  |
2024 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 21/30 |  |
2023 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 16/30 |  |
2022 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 23/30 |  |
2021 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 16/30 |  |
2020 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 25/30 |  |
2019 | Intro To Deep Learning (3c) Qiang Ji Seats Taken: 27/40 |  |
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