Advanced Computational Tools 
Machine Learning with OWL
SUPERVISED, UNSUPERVISED AND REINFORCEMENT
Faculty: Mateusz Zwierzycki

The workshop will introduce participants to a range of machine learning methods, such as neural networks, clustering, autoencoders and Q-learning. These tools will be applied to the specific parametric models, which will yield solution spaces to be traversed, analysed and explored. Owl plugin and library will be used to parse and preprocess various data sets available in the public domain. The activities of the participants will be structured as a series of exercises, which will gradually unveil the greater picture of the machine learning as a design tool.

Learning Objectives
At course completion the student will:
– understand a range of machine learning methods
– be able to couple parametric models with machine learning tools
– setup unsupervised, supervised and reinforcement learning using Grasshopper
– be aware of data preprocessing necessary for successful training