Machine learning for construction machines

Machine learning is a branch of artificial intelligence that is used to solve complex tasks and technical applications. The basic concept is to design models based on data and information in such a way that predictions can be made using a model. In machine learning, algorithms are trained to classify information, recognize patterns in data sets and make well-founded decisions. Applications for machine learning can be found in various areas, but the use of machine learning in the construction industry is a relatively new topic.

In this workshop, the basics of machine learning will be discussed and explained using various examples. The examples range from simple regression models to complex neural networks approaches for the realization and implementation of filters, controllers, etc.

• Machine Learning basics, underlying concept, introduction to neural networks
• Reinforcement Learning paradigms and its potential
• The potential of AI (Machine Learning) in construction machine applications
• Simple exercises, introduction to the framework PyTorch, basic understanding of an AI approach to engineering problems.

• A solid grounding in higher mathematics is not a prerequisite, but it is nevertheless advantageous
• Programming in Python or another OOP is strongly recommended

The aim of this workshop is to teach the basic principles of machine learning for use in real applications. The workshop provides the basis for deepening further A.I. paradigms (deep learning, reinforcement learning). In addition, the workshop teaches the basics of data analysis and the machine learning mindset: data collection, pre-processing, model design, loss function, etc.