| Deep Learning - 11512 - CS 69000 - DPL |
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Associated Term: Spring 2020
Levels: Graduate, Professional, Undergraduate West Lafayette Campus Lecture Schedule Type Learning Outcomes: This is an in-depth course on deep learning. Please consider other offers on campus if you are only interested in deep learning as a tool. A lot of our coding will be in pytorch as a numpy library for GPU. Upon completing the course, students should be able to: - Understand the mathematical foundations of deep learning, including: - Foundations of Representation Learning - Stochastic optimization of neural network models - Backpropagation and Backpropagation-Throuh-Time - Feedforward Networks, Recurrent Networks, Transformers, Set -Representations, Convolutional Networks - Graph Neural Networks and Embeddings (e.g. word2vec, Glove) - Variational Auto-Encoders - Multi-task Learning, Transfer Learning, and Meta Learning - Extrapolations and Causal Mechanisms - Markov chains and Sequence learning - Generative Adversarial Networks - Understand how to evaluate the performance of neural networks, as well as formulate and test hypotheses - Understand how algorithmic elements interact to impact performance Required Materials: Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press Technical Requirements: View Catalog Entry
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