Summer School in Modern Computing Paradigms for Information Security Key areas: Quantum computer programming and tensor flow/neuronal network programming
9 - 20 July 2018
The Summer School will be held 9 - 20 July 2018 at our School of Informatics, Communications and Media in Hagenberg.
This Summer School gives you an excellent opportunity to enhance your knowledge and at the same time explore Upper Austria, meet new people, and expand your network internationally. You can earn 6 ECTS for this summer school.
We are inviting Master's students from partner universities with a Bachelor Degree in Computer Science to join us for this international event!
Students from partner institutions are free of charge! Students who are not from a partner university have to pay a tuition fee in the amount of 350€ (for both weeks).
All students have to bear the costs for housing and travelling costs.
In addition to lectures, workshops and laboratory tutorials, our programme will include site visits and two days filled with social and cultural activities.
Summer School Content:
New programming paradigms have made big advances in the last years.
Machine Learning is a promising new way to solve problems, also related to security questions, that are hard to tackle using classical computing approaches. Two main factors, the availability of powerful hardware to do the learning, and the availability of large amounts of data to learn from, have led to big advances in various areas.
From a programming paradigm point of view, by using algorithms that learn from examples, machine learning enables a computer to gain insights without being explicitly programmed.
Quantum Computing promises to solve many hard problems more efficiently than classical computation. Computation is preparing a quantum system, transforming it, and finally measuring the system thus producing an output. A clever combination of transformations makes use of properties of quantum superposition and entanglement.
What to expect:
Learn about the theoretic background needed to understand the methods used in machine learning and quantum computing in the lectures.
Apply your theoretical knowledge and learn to use the tools to solve small problems in exercise sessions. Implement machine learning solutions using TensorFlow on a GPU-accelerated machine. Learn how to model quantum circuits. Depending on the availability of the simulation hardware at the time of the Summer School we plan to let you simulate these circuits on a quantum computer simulator.
Work on a more advanced problem related to security questions, in a small group of students, implement your ideas and evaluate, present and discuss your solution.
Some experience with the Python programming language is expected, but no knowledge about neural networks, TensorFlow, and quantum computing. Basic linear algebra and calculus will be helpful for understanding the mathematical concepts behind both Deep Learning and Quantum Computing.