DATA 621 - Neural Networks & Deep Learning

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This course covers deep learning and neural networks, offering a comprehensive exploration of both foundational concepts and cutting-edge advancements in the field. Designed for students with a solid background in machine learning, this course aims to equip students with the advanced skills needed to develop, understand, explain, analyze, and implement neural network models that can tackle complex real-world problems. Throughout the course, participants will engage with a wide array of topics, including but not limited to, the architecture of deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), explainability techniques, and generative adversarial networks (GANs). Special emphasis will be placed on understanding the theoretical underpinnings of these models, their optimization techniques, and how they can be applied to diverse areas such as image and speech recognition, natural language processing, and autonomous systems. Cross-listed with DATA 442