ECE 3405: Foundations and Trends in Machine Learning for Engineering

Machine learning has achieved huge success in many engineering applications such as computer vision, gene discovery, financial forecasting, credit card fraud detection, autonomous vehicle navigation, biomedical signal modeling, wireless/radar/aerospace systems and others. The course will briefly review discrete-time signals and systems, including convolution and Fourier transforms. This course will introduce the fundamental concepts, algorithms, and theories in machine learning, including linear models, projection/nonlinear embedding methods, neural networks/deep learning, parametric/non-parametric methods, kernel machines, mixture models, and pattern recognition/classification. Also, the lectures will briefly summarize recent trends in the field to provide students with cutting-edge knowledge for engineering. The course will give the student the basic ideas and intuition behind these methods, as well as a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning techniques and apply them in one or more application-based projects.