Master Meetings

16.05.2023

Data Analytics

Differently from other data mining courses, in this course we deal with datasets that for their large size, fast speed of updating, and variety of content (all characteristics of Big Data) cannot be mined with standard techniques. Hence, the course deals with topics such as: similarity measures for very large datasets, mining fast data streams, link analysis, clustering, recommender systems, etc.

Computer Vision and Pattern Recognition

The course covers the following topics: image formation (from both a photometric and geometric perspective), low-level imaging methods (filtering and edge detection), single and multi-view geometry for 3D reconstruction, feature extraction for object recognition. Lectures are accompanied by various examples of applications where these methods apply, and hands-on programming exercise to solve real-world problems.

Image and Video Processing

The course will expose students to all stages of the image and video processing pipeline. Starting from acquisition, the students will learn how images are captured and represented. They will learn about sampling theory, different image and video formats, and the basics of color theory. Next, the students will be introduced to various image and intensity transformations as well as filtering. The course will discuss Fourier transform and concepts such as convolution, high- and low-pass filter in both primary and frequency domains. The above topics will build the foundation for more advanced topics in image and video processing, such as restoration and enhancement. To this end, the course will also discuss different image decomposition techniques such as Gaussian, Laplacian pyramids, wavelet transform, and more advanced filtering strategies such as cross-bilateral filtering. Final lectures will introduce students to the most recent developments in image and video processing, which involve machine learning techniques. The students will learn about basic techniques which exploit neural networks in the context of image and video processing.

Philosophy and Artificial Intelligence

This course will provide an introduction to AI and to the impacts of AI on the wider world. It is designed to be of interest to both philosophers and those with a background in computer science. It will cover topics such as the following:

- Defining intelligence (Can we compare human and animal intelligence with the sort of intelligence can be achieved on the part of a machine?)

- The Turing test (Why, after more than 50 years, we are still so often disappointed when we telephone our bank and are put through to a machine?)

- Consciousness (Can a computer have a conscious mind? Can it have emotions and desires? Can it have a will?)

- Deep neural networks (Could we build an intelligent machine by replicating the structure of the human brain?)

- AI ethics (What could it be for a machine to behave in an ethical or unethical manner? Will there, one day, be robot cops?)

- The Singularity (Could we build a machine with superhuman intelligence, which could in turn design an even more intelligent machine, thereby initiating a chain of ever more intelligence machines which would one day have the power to take over the planet?)

- Digital immortality (Could we, one day, find a way to upload the contents of our brains into the cloud so that we could live forever?)

- The meaning of life (If routine, meaningless work in the future is performed entirely by machines, will this make possible new sorts of meaningful lives on the part of humans?)

Quantum Computing

Followed by an introduction to the basic principles of quantum physics, such as superposition, interference, or entanglement, a variety of subjects are treated.