Seminar: Rotating Topics Spring 2025 Deep Learning

Catalog description

This course focuses on theoretical foundations and practical applications of deep learning, the subfield of machine learning concerned with large neural networks trained on large data sets. Topics include training models by stochastic gradient descent, implementing various neural network architectures, and choosing network hyperparameters. Application areas include classification, regression, and reinforcement learning problems. Students will implement their own neural networks from scratch and get experience using state-of-the-art deep learning libraries. Satisfies Applications elective in the Computer Science major and minor. Counts an an elective in the Data Science inerdisciplinary minor. Image Processing Instructor Peck This project based course will introduce students to digital image processing techniques such as compression, feature extraction, and edge detection. Students will explore and compare multiple image processing algorithms, evaluate their efficiency, and design and implement multiple image processing algorithms. Prerequisites & Notes Prerequisites: CSC 221, MAT 150, and the ability to program in a high-level language such as Python, Java, or C++ at the level expected in CSC 221.