Data-driven AI for Decision-makers 5 Credits

The course is aimed at decision-makers, in both industry and the public sector, who wish to gain knowledge about how data-driven AI can be utilized in organizations. The starting point of the course is on how business problems and decision-making in organizations relate to different tasks in data-driven AI. From this, the course explores how data-driven AI projects are conducted and evaluated. Participants will see examples from a variety of domains and problem types, to gain an understanding of how general approaches can be applied in different situations. The course also contains an overview of modern AI techniques and how these are used for different data analysis tasks. Practical experience in both project design and using AI techniques for data analysis will be given in workshops, seminars and project work. Throughout the course, emphasis will be placed on discussing both the technical aspects and the wider implications of using data-driven AI for decision support.

Course Contents

The course is aimed at decision-makers, in both industry and the public sector, who wish to gain knowledge about how data-driven AI can be utilized in organizations. The starting point of the course is on how business problems and decision-making in organizations relate to different tasks in data-driven AI. From this, the course explores how data-driven AI projects are conducted and evaluated. Participants will see examples from a variety of domains and problem types, to gain an understanding of how general approaches can be applied in different situations. The course also contains an overview of modern AI techniques and how these are used for different data analysis tasks. Practical experience in both project design and using AI techniques for data analysis will be given in workshops, seminars and project work. Throughout the course, emphasis will be placed on discussing both the technical aspects and the wider implications of using data-driven AI for decision support.

The course includes the following elements:
- Introduction to data-driven AI: terminology, context and evaluation from a business perspective
- Process model for data-driven AI, from business problem to deployment
- Problem types and tasks in AI, related to common business problems and decision-making situations in organizations: prediction, clustering, association rules, anomaly detection, sentiment analysis, text and picture analysis
- Overview of techniques for data-driven AI: decision trees, similarity-based techniques, support-vector machines, neural networks, ensemble models
- Data sources: pre-processing and data quality
- Practical work in a software tool
- Ethical and legal aspects of using data-driven AI in organizations

Prerequisites

Passed courses of at least 40 credits in a main field of study within Engineering and Technology, Natural Science or Social Sciences, and at least 1 year of work experience (or equivalent). English proficiency is required (level 6 or equivalent).
Applicants that have at least 4 years of work experience in the industry are exempt from the requirement of at least 40 credits within Engineering and Technology, Natural Science or Social Sciences field.

Level of Education: Master
Course code/Ladok code: TDDR24
The course is conducted at: School of Engineering Last modified 2023-12-21 11:19:51