T 6:10pm–8:00pm · W 8:10pm–10:00pm
2 distinct times across 2 sections.
2 of 2 sections have room
Description
Deep Learning has become a cornerstone of Artificial Intelligence (AI), with applications in finance, healthcare, sports, autonomous vehicles, chatbots, national security, and artistic creations using elements of Natural Language Processing, Computer Vision, and Speech Recognition. Students will gain a solid foundation in Deep learning and its applications, starting with a compressed review of some Statistical Learning models followed by much deeper dive into Deep Neural Networks. Topics covered include Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures and Generative Adversarial Networks (GAN). Students will also learn training of agents to make optimal decisions in complex environments using Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate these models.
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Sections2
- 31 of 45 seats left
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
- 1 of 45 seats left
Seat data provenance: Directory as of August 23, 2026 at 4:00 AM
In your week
Workload and grading
- Grading
- Not published
- Format
- Not published
- Credits
- 3 credits
Reported workload
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