The purpose of the course “Artificial Intelligence for Managers” is to give managers and decision makers a principle understanding of AI and to increase their understanding of opportunities, difficulties, benefits, and risks connected to AI. It is neither an “Introduction to AI” nor an “AI for dummies” course. Instead, it is set to demystify AI and to transform it into an actionable tool for manages and decision makers.
Target group This course is for product managers, project managers, executives, and engineering managers in organizations that have already made, or are about to make, the transition to working with AI.
Content The course is organized in three modules. The initial module will focus an introduction to AI, giving an understanding of what type of cases can be addressed with AI and what managers need to know about AI technology. Module two will cover tools and concrete on how to set up an AI strategy and roadmap, how to get started on AI projects, how to integrate AI and IT development, how to (self) evaluate AI in use, and, not to forget, the ethical and legal aspects of AI. The third module will give the participants the chance to use their new knowledge and tools and work with their own practical cases and how they could be addressed using AI.
The goal of the course is to empower the participants to:
Describe the principal concept of AI, its strengths, and shortcomings
Understand opportunities, myths, and pitfalls of AI
Identify problem areas in industry, society, and in management where AI could be utilized
Analyze how AI can be applied in a particular problem area
Manage an AI strategy and get started: implement a strategy and a roadmap to apply AI in a particular problem area
Understand how to integrate AI with IT development
Assess the maturity of AI utilization in an organization
Reflect on applications of AI from an ethical and legal perspective as well as the future challenges (technical, organizational, social, etc.)
Practical information All materials will be accessible and include reading material, lecturer slides etc. The lectures can either be attended live via Zoom or later using the recordings at a time that is convenient for the participants. There will be 3 onsite workshops with a focus on interaction with the teacher and the co-participants of sharing real-life experiences and insights. The course will be delivered in a flexible manner to facilitate the combination of course work with your ongoing professional commitments.
The total effort to pass this course is typically around 200 hours.
Teaching language: English
Entry requirements The basic eligibility for this course is a bachelor’s degree. Candidates with corresponding work experience are also invited to apply. Two years of relevant work experience is considered equivalent to one year of university studies at bachelor level.
Today, the explosion of data has created new opportunities to apply machine learning (ML). Handling of the large amounts of data created by the very rapid digitization would not be possible without Machine Learning (ML). The purpose of the course "Introduction to Machine Learning" is to give you the foundation for ML. You will get an introduction to the basic areas of ML: data, statistics and probability for ML.
The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called ‘Big data’ being produced every day at exponential growth.
The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.
The course will give insights in fundamental concepts of machine learning and actionable forecasting using predictive analytics. It will cover the key concepts to extract useful information and knowledge from big data sets for analytical modeling
Örebro University is offering an introduction course in artificial intelligence. The course will address the basic concepts within classical artificial intelligence (other than machine learning). Traditional artificial intelligence is characterized by the so-called declarative approach to problem solving. The course deals with a selection of different intelligent problem-solving methods, both in theory and practice.
After completing the course, the student will be able to model and use appropriate generic solution algorithms to solve problems in an intelligent system. The course is part of the education initiative Smarter at Örebro University.
Örebro University is offering an introduction course in machine learning. The course will offer knowledge of the basic concepts with machine learning, the selection and application of different machine learning algorithms as well as evaluation of the performance of these learning systems.
After completing the course, student should be able to prepare data and apply machine learning techniques to solve a problem in an intelligent system. The course is part of the education initiative Smarter at Örebro University.