This course is designed to give students an in-depth introduction to deep learning and review the state of the art in important applications, such as computer vision and language modeling.
The lectures and exercises are designed around fundamentals and use cases and will use real-world data to gain practical experience with machine learning models and algorithms.
The course introduces basic concepts of machine learning and empirical data analysis, such as model fitting, selection and validation. During the course, students discuss supervised machine learning, starting with linear models, to non-linear models, including deep neural networks, convolutional neural networks and sequence models. Additionally the course discusses unsupervised learning, generative models and self-supervision.
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