This course will enable participants to successfully understand theoretical and practical perspectives on implementation of machine learning and artificial intelligence.

Participants will gain competence in the data and machine learning process, supervised and unsupervised learning, deep learning, reinforcement learning, reproducing models through transfer learning, testing and evaluation of models, and explainability of model output. They will gain insight into challenges and applications of anomaly detection, decision support, automated tasks and autonomous decision-making processes.

After completing the course, students should be able to explain how the most common machine learning algorithms work, understand how artificial neural networks and deep learning can be utilized in practice, gain knowledge on different tools and implementations, and use machine learning as a tool to identify and utilize information in an effective way.

Subject description 2322

Utvikling og anvendelse av intelligente systemer

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