Deep Learning is rapidly emerging as one of the most successful and widely applicable sets of techniques across a range of domains, including vision, language, speech, robotics, medicine, and AI in general. This has led to significant success and exciting new directions that may previously have seemed out of reach.
This course offers an introduction to the fundamentals of deep learning, covering both theory and applications. It starts from the basics of Neural Networks (NNs) and extends to some of the latest research. Topics covered include neural net architectures (MLPs, CNNs, RNNs, transformers, large language models, generative models), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization, self-supervised learning and robust learning, as well as applications to computer vision, natural language processing, medicine, and science, among others. The course will be delivered through instructor lectures and reinforced with coding assignments that teach both theoretical and practical aspects. Additionally, it will include a project that allows students to explore an area of deep learning that interests them in more depth.
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