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INTRODUCTION TO DEEP LEARNING AND GENERATIVE MODELS

STAT 453
Course Description

Deep learning is a field that specializes in discovering and extracting intricate structures in large, unstructured datasets for parameterizing artificial neural networks with many layers. Since deep learning has pushed the state-of-the-art in many research and application areas, it's become indispensable for modern technology. Focuses on a understanding deep, artificial neural networks by connecting it to related concepts in statistics. Beyond covering deep learning models for predictive modeling, focus on deep generative models. Besides explanations on a mathematical and conceptual level, emphasize the practical aspects of deep learning. Open-source computing provides hands-on experience for implementing deep neural nets, working on supervised learning tasks, and applying generative models for dataset synthesis.

Prerequisites

MATH 320 , MATH 321 , MATH 340 , MATH 341 , MATH 345 , MATH 375 , graduate/professional standing, or declared in Statistics VISP

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.66

3.06% from Historical

Completion Rate
98.88%

-0.12% from Historical

A Rate
50.56%

3.91% from Historical

Class Size
89

19.46% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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