This course provides an introduction to sensory information processing for machines. It covers visual, audio, language, and haptic perception for artificial systems. The course provides the fundamental signal processing background, mainstream machine learning methodologies, and the background and analogies for human perception.
Topics
- Perception basics for visual, audio, speech, and haptic perception
- Representation of sensory information
- Receptive field measurements
- Linear and Fourier theory
- Invariant transformations
- Combining information streams
- Error and uncertainty propagation
- Machine learning principles for sensory information processing
- Experimental evaluation and design
Learning Outcomes
Students are able to understand and evaluate signal processing methodologies for various sensory modalities and their relation to human perception. They acquire a basic knowledge of machine perception and are able to apply machine perception methods in relation to human perception.
