Learning Outcomes
After completeing the module the students will have a solid understanding of theoret- ical foundations of Machine Learning and will be able to develop, apply, and analyze the complexity of the resulting learning algorithms. Moreover, a special emphasis will be put on applications of Machine Learning in areas such as Signal Processing and Wireless Communications and the students will be able to theoretically analyze and algorithmically solve learning problems arising in these fields.
Content
The learning content includes:
• Learning Model
• Learning via Uniform Convergence
• Bias-Complexity Tradeoff
• Stochastic Inequalities and Concentration of Measure
• Suprema of empirical Processes
• Vapnik- Chervonenkis Dimension (VC Dimension)
• Nonuniform Learning
• Runtime of Learning
• Hilbert Spaces and Projection Methods
• Kernel and Multi-Kernel Methods
• Information Innovation
• Regularization, Dimension Reduction and Compressive Sensing
Description of Teaching and Learning Methods
The module consists of conventional frontal teaching in class, developing theoretical and mathematical concepts, exercises developed in class, in order to develop problem- solving skills and reinforce comprehension of the theory,
