Deep Learning - CO460
The course covers the fundamental concepts and advanced methodologies of deep learning and relates those to real-world problems in a variety of domains. The aim is to provide an overview of different approaches, both classical and emerging.
This course teaches the necessary skills that enable students to work and conduct research in the field of deep learning, covering both basic and classical techniques, as well as the most recent developing and cutting edge results.
Mathematics for Machine Learning - CO496
The aim of the course is to provide the students the necessary mathematical background and skills in order to understand, design and implement modern statistical machine learning methodologies, as well as inference mechanisms. The course will provide examples regarding the use of mathematical tools for the design of basic machine learning and inference methodologies, such as Principal Component Analysis (PCA), Bayesian Linear Regression and Support Vector Machines.