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Decision making with artificial and computational intelligence

1BM120

About this course

In modern businesses, the extraction of information from the data in the information systems has become a competitive advantage. The availability of large amounts of data of different modalities (i.e. big data) often requires the use of advanced analytics, computational models and intelligent methods for discovering the relevant information that a business needs. In this course, students are introduced to the advanced data analysis methods from computational and artificial intelligence. Many models must be adaptive to the data and the underlying processes that generate the data. Adaptive and learning models based on neural networks are discussed for exploiting the past information in order to improve the design of information systems for business processes by providing them with knowledge about past business experience and, consequently, to improve their decision support capability. In this context, we will also discuss different approaches to learning, such as supervised, unsupervised, and deep learning. Deep reinforcement learning, which combines deep learning and reinforcement learning, is considered for solving sequential decision-making problems. Optimization of operational processes through nature-inspired meta-heuristics (based on evolutionary computation, swarm intelligence, and multi-objective optimization techniques) is considered.

Learning outcomes

After following this course, the student is able to:

  • discuss how computational and artificial intelligence techniques can support decision making frameworks in different business environments;

  • use deep neural networks, reinforcement learning and evolutionary computation to learn from past business experiences and generate new understanding and knowledge;

  • apply nature-inspired meta-heuristics for optimization of operational processes.

Prior knowledge

You must meet one of the following collections of requirements

  • Collection 1
  • Completed Final examination Bsc program
  • Collection 2
  • Completed Pre-Master

Resources

  • Python (Anaconda)
  • Scientific papers and book chapters (links available via TU/e library)

Additional information

  • Credits
    ECTS 5
  • Level
    master
If anything remains unclear, please check the FAQ of TU Eindhoven.

Offering(s)

  • Start date

    21 April 2025

    • Ends
      22 June 2025
    • Term *
      Block GS4
    • Location
      Eindhoven
    • Instruction language
      English
    • Register between
      15 Nov, 00:00 - 23 Mar 2025
    Enrolment starts in 7 days
These offerings are valid for students of Wageningen University