Deep Learning

The course covers the theory and practice of deep learning. Students will learn the foundational mathematics behind deep learning and explore topics such as multi-layer perceptrons (MLPs), back-propagation and automatic differentiation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative models, and the role of data in training models. These techniques play a crucial role in modern AI systems, including image and video understanding, natural language processing, generative AI, robotics, medicine and scientific discovery. The various practical assessments will enhance student's understanding and intuition of deep learning and its application.