Application of Computational Intelligence Methods

Introduction of modern computational intelligence methods (evolutionary algorithms, machine learning and related fields) and their application to solving of real problems. Basic knowledge of machine learning, neural networks and evolutionary algorithms is required.

Teach advanced methods combining evolutionary algorithms, neural networks, and other computational intelligence methods. Deepen the knowledge from the introductory courses on neural networks, machine learning, data mining and evolutionary algorithms. The seminar will be focused on working with real data and the cooperation of various methods while solving difficult problems from the areas of optimization, learning and modelling.

Additional study resources/papers

Deep Style:

• https://arxiv.org/abs/1508.06576
• DeepDream blog https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html

Fooling Neural Networks:

• https://arxiv.org/abs/1412.1897
• https://arxiv.org/abs/1412.6572

Neural Machine Translation:

• https://arxiv.org/abs/1409.0473
• https://arxiv.org/abs/1412.2007

Image Super-Resolution:

• https://arxiv.org/abs/1511.04491
• https://arxiv.org/abs/1603.08155

Deep reinforcement learning:

• https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf
• https://arxiv.org/abs/1509.06461
• https://arxiv.org/abs/1611.05397
• https://arxiv.org/abs/1602.01783

AlphaGO:

• http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html

Generative Adversarial Networks

• https://arxiv.org/abs/1406.2661
• https://arxiv.org/abs/1611.06430

Model calibration

• https://www.cs.cornell.edu/~alexn/papers/calibration.icml05.crc.rev3.pdf
• http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.13.7457&rep=rep1&type=pdf