Inhalte

This course addresses state-of-the-art computer vision algorithms that let computers see, learn, and understand image and video content. After being taught the required basics in machine learning, students will - accompanied by practical exercises - get to know the most promising techniques.

The topics of the course may be summarized as follows:

• Machine learning foundations
• Deep learning, with a focus on CNNs and current reference architectures
• Data reduction (quantization, dimensionality reduction)
• Traditional computer vision (hand-crafted features and algorithms)
• CNN-based computer vision

The learned concepts will be illustrated by successful examples in practice. The accompanying exercises will contain some hands-on assignments. Towards the end of the course more advanced topics in object detection and object recognition will be addressed.

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Hinweis: Diese Vorlesung ersetzt die frühere Vorlesung ?Multimedia II“, kann jedoch genauso eingebracht werden.

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?bungen

Es erscheint w?chentlich ein ?bungsblatt zu den behandelten Vorlesungsinhalten. Jedes ?bungsblatt wird in der Globalübung?der folgenden Woche besprochen. Es gibt keine Abgabe / Korrektur von ?bungsbl?ttern.

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Literatur

• M. Mitchell.?Machine Learning. McGraw-Hill Science/Engineering/Math, 1997; Chapters 1-8; ( PDF)
• Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2016, ISBN-13: 978-
  0262035613; Chapters 2-5 are a must read! ( PDF)
• David A. Forsyth and Jean Ponce.?Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River, New Jersey 07458.( PDF)

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