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| CS 58700 - Foundations Of Deep Learning |
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Credit Hours: 3.00. This course provides an integrated view of the key concepts of deep learning (representation learning) methods. This course focuses on teaching principles and methods needed to design and deploy novel deep learning models, emphasizing the relationship between traditional statistical models, causality, invariant theory, and the algorithmic challenges of designing and deploying deep learning models in real-world applications. This course has both a theoretical and coding component. The course assumes familiarity with coding in the language used for state-of-the-art deep learning libraries, linear algebra, probability theory, and statistical machine learning.
3.000 Credit hours Syllabus Available Levels: Undergraduate, Graduate, Professional Schedule Types: Distance Learning, Lecture Offered By: College of Science Department: Computer Science Course Attributes: Upper Division May be offered at any of the following campuses: West Lafayette Learning Outcomes: 1. Understand statistical Foundations of Deep Learning. 2. Understand feedforward Networks. 3. Understand stochastic optimization of neural network models. 4. Understand Bayesian Neural Networks. 5. Understand invariant & Equivariant Representation Learning. 6. Understand task-invariant representations. 7. Understand meta Learning. 8. Understand multi-task Learning. 9. Understand transfer Learning. 10. Understand implicit generative models (probabilistic models without explicit likelihoods). 11. Understand variational Auto-Encoders. 12. Understand generative Adversarial Networks. 13. Understand stable Diffusion Generative models. 14. Understand how to evaluate the performance of neural networks, as well as formulate and test hypotheses. 15. Understand how theory and algorithmic elements interact to impact performance. |
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