DATA-645 Neural Networks and Deep Learning (3)

This course provides a comprehensive introduction to the foundations and applications of deep learning within the broader context of artificial intelligence (AI). Students gain hands-on experience designing, training, and evaluating neural network architectures using modern Python libraries. Core topics include feedforward, convolutional, and recurrent networks, optimization methods, and regularization techniques. The course builds toward state-of-the-art AI systems, exploring cutting-edge models used in computer vision, time series forecasting, and natural language processing. Emphasis is placed on understanding when and how to apply deep learning effectively, recognizing its limitations, and interpreting model behavior. Crosslist: DATA-445 . Prerequisite: CSC-680 , CSC-681 , DATA-642 , or STAT-627 . Note: Experience in Python programming language recommended.
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