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The course will introduce machine learning methods for image analysis, with a focus on deep learning and probabilistic graphical models. The aim is to convey state of the art methodology for solving problems like image classification, semantic segmentation, instance segmentation, object detection, and object tracking. Topics will include supervised learning with convolutional and recurrent neural networks, as well as structured prediction with discrete Markov random fields and integer linear programming. Prerequisites that go beyond a basic knowledge of linear algebra, analysis and probability theory will be covered. |
