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.
Spring | Summer | Fall | ||
---|---|---|---|---|
(Session 1) | (Session 2) | |||
2023 |
Intro To Deep Learning (3c)
|
|||
2022 |
Intro To Deep Learning (3c)
|
|||
2021 |
Intro To Deep Learning (3c)
|
|||
2020 |
Intro To Deep Learning (3c)
|
|||
2019 |
Intro To Deep Learning (3c)
|
|||
2018 | ||||
2017 | ||||
2016 | ||||
2015 | ||||
2014 | ||||
2013 | ||||
2012 | ||||
2011 | ||||
2010 | ||||
2009 | ||||
2008 | ||||
2007 |