Machine Learning for Transport and Multi-Machine Systems

The course gives an introduction to the basic concepts of machine learning (ML) (supervised, unsupervised, reinforcement learning) and puts them into relation to artificial intelligence and data analytics. Important fundamental machine learning algorithms including regression, classification, clustering, and neural networks are introduced and implemented in Python. The course also gives a brief overview of more advanced ML techniques that are used in the transportation and multi-machine domains such as reinforcement learning, graph-neural networks, and recommender systems. On this basis, machine learning methods are applied in group projects to transport and multi-machine systems using different types of data from different domains (e.g., AIS, GPS, New York Taxi, or machine failure data).