ECE 457B - Computational Intelligence

Offered Winter 2025 by Prof. Mark Crowley

Course Description

This course provides a rigorous examination of machine learning, structured to progress from fundamental principles to advanced methodologies. It encompasses a broad spectrum of supervised learning topics, including nonparametric and parametric models, linear and nonlinear approaches, deep learning paradigms, and probabilistic models. In the domain of unsupervised learning, students will delve into clustering, density estimation, and dimensionality reduction, while reinforcement learning will be introduced preliminarily. Assignments, an essential part of the curriculum, enable students to explore the intricate applications of machine learning. Theoretical knowledge is translated into practice through tutorials on industry-standard tools such as PyTorch and Keras. Interactive classroom discussions will pivot around both the practical trade- offs inherent in real-world applications and the theoretical underpinnings that provide an intuitive grasp of the field.

Course Outline

See the official course outline for course location, times, staff contact and other information.

Additional Resource Links

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