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In the era of intelligent assistants, autonomous agents, and self-driving cars we expect AI systems to not cause harm and withstand adversarial attacks. In this course you will learn advanced methods of building AI models and systems that mitigate privacy, security, societal, and environmental risks. We will go deep into attack vectors and what type of guarantees current research can and cannot provide for modern generative models. The course will feature extensive hands-on experience with model training and regular discussion of key research papers.

Machine learning is the computational study of artificial systems that can adapt to novel situations, discover patterns from data, and improve performance with practice. This course will cover the mathematical foundations of supervised and unsupervised learning. The course will provide a state-of-the-art overview of the field, with an emphasis on implementing and deriving learning algorithms for a variety of models from first principles.

This course will provide an introduction to, and comprehensive overview of, reinforcement learning (RL).

People are able to infer the characteristics of a scene or object from an image of it. In this course, we will study what is involved in building artificial systems which try to infer such characteristics from an image. Topics include: Basics of image formation - the effect of geometry, viewpoint, lighting and albedo on image formation. Basic image operations such as filtering, convolution and correlation. Frequency representations of images. The importance of scale in images. Measurements of image properties such as color, texture, appearance and shape.