Introduction to Artificial Intelligence

Artificial-intelligence-driven technologies are becoming a central part of an increasing number of products, ranging from self-driving cars and home assistants to business automation and customer management. Being able to design products and tools based on this technology and evaluate their impact is a complex skill that requires an overview of the latest market trends and emerging technologies. Nevertheless, the public discussion about AI technologies is often veiled in impenetrable jargon and confusing media hype.

The purpose of the course is to introduce the students to the fundamental concepts that modern AI is built on. The students will build a solid vocabulary and intuition that will allow them to recognize the most common ways AI technology can be useful to a business. Armed with the conceptual vocabulary, the students will work with case studies that show how AI has been implemented across multiple industries. The case studies will reflect the concepts taught in the first part within the business context.

The maturity of a certain technology will inform the cost projection and the success risk in any business. Therefore, assessing the maturity of an AI technology is essential when deciding on the adoption of the technology within an organization. The course contains a chapter on AI tech maturity and discusses two business case studies.

AI is a powerful technology that can bring a lot of value to an organization but is not without its risks. The final course chapter discusses potential risks that can undermine the goals of an organization. The students will learn about common pitfalls and be ready to ask relevant questions to mitigate the risk of the technology.


With the course you will achieve the following competencies::

  • Assess the maturity of AI technologies,
  • Recognize opportunities of how AI can augment business intelligence, cut costs and automatize within any organization,
  • Understand the impact of cutting-edge AI research,
  • Learn how to develop and successfully communicate AI on a strategic level in your company,
  • Asking the right questions when estimating costs and benefits of developing AI solutions,
  • Find key products and services in the AI industry,
  • Recognize key skills needed to execute your company’s AI strategy.


Course content

The course as four chapters divided into two days. Day one will cover "Introduction and Fundamentals" and "Case studies of successful AI application" while day two will cover "Technology maturity" and "Risk considerations"

Day 1

Chapter 1: Introduction and Fundamentals

In the first part, we will discuss what it means for something to be considered AI. Basic concepts are introduced followed by a short example to solidify the knowledge. We will discuss students’ backgrounds and their expectations from the course together with the learning outcomes.

  1. Introduction to fundamentals of modern AI
    1. Classification & Regression (Supervised learning)
      Answers to a problem can already be present in a database of previous instances when a similar decision had to be made. Supervised learning can automatize the decision process by leveraging the patterns seen in the past. We will learn how classification works on a simplified spam filter, and how regression works on a problem of real-estate price prediction.
    2. Clustering (Unsupervised Learning)
      Data can hide an unseen structure that may be useful when reasoning about a market. Clustering methods can uncover such structures. We will see how clustering can find customer segments for more effective marketing campaigns.
    3. AI agents (Reinforcement Learning)
      AI promises to replace human agents in highly repetitive and high-frequency tasks. We will get insights into how AI agents work and how the gaming industry uses them to automatize product testing and quality control.
    4. Optimization
      Designing a product or a system requires designers to be creative within bounds defined by some cost metric. Whether it is resource optimization in architecture or scheduling within an organization, AI can deliver superhuman performance while minimizing the resources needed to realize it. We will learn how Autodesk augments product design with its AI services.
    5. Deep learning
      Deep learning and neural networks have garnered extensive media coverage. We will learn what are neural networks and how are they used. We will learn how can neural networks recognize purchasable objects on an image and take customers to relevant stores.

Chapter 2: Case studies of successful AI application
In this part, we will learn which problems companies tackled by deploying AI and how it influenced their business. Using the conceptual vocabulary built in the previous chapter we will be able to organize them into families of problems to help us recognize opportunities in our organizations.

We dive into four cases in this session:
  1. Fin-tec
    AI used in fraud detection and client creditworthiness
  2. Retail
    AI used in virtual dressing rooms and Next Best Action agents for customer acquisition
  3. Food delivery
    AI used in courier to order allocation
  4. Health care
    AI used to extract relevant information from medical literature and pathology reports
  5. Q&A session

Day 2

Chapter 3: Technology maturity
We will discuss the steps of AI technology maturity. Examples of steps being fundamental AI R&D, emerging technology, early business adoption, media hype stage, technology dead-end, or the final technology maturity. We will discuss at which point are risk minimal in business applications? How to assess if certain technology is ready for your business?
Case-study: Failure of IBM Watson
Case-study: Chatbots - advantages and limitations

Chapter 4: Risk considerations
There is a myriad of potential risks that AI introduces in a business. We will learn about the most common risks and discuss how to assess their cost and mitigate them.

  1. Data gathering
    Certain AI systems need a large volume of data to operate successfully. If data is not present the acquisition can introduce additional costs to AI integration.
  2. Data cleaning
    Historical data often does not exist in format and structure useful for AI applications. Assess whether resources needed to transform data to a usable state are worth the benefits.
  3. Model interpretability
    To avoid unintended consequences of AI automatization, insight is needed into the inner workings of AI models and algorithms. Unfortunately, complex AI systems are often opaque to interpretation introducing unforeseen risks.
  4. Employee distrust
    Complete automation with AI can introduce a sense of losing control and importance over the work process in the employees. Employee distrust can lead to underutilization of the deployed AI systems introducing losses in the work process. Learn how to mitigate the lack of trust among employees and combine their talents with automated solutions.
  5. Bias and error magnification
    AI that depends on big data can be as good as the said data. Poor quality and highly biased data can amplify those biases and errors. Learn what it means for the data to be high quality to avoid perpetuating historical problems within a business.
  6. Loss of key skills in the workforce
    Overreliance on automated systems can lead to the loss of skills among human talent. Hear about concerns that experts expressed that pilots are losing basic flying skills due to cockpit automation. Consider which skills are potentially being lost due to AI automation and are they worth keeping within the employees.
  7. Q&A session


Djordje Grbic

Djordje Grbic is a Postdoc in the Creative AI Lab at the IT University of Copenhagen. He holds a MSc degree in Computer Science from the University of Zagreb, where he developed an AI application to optimize the university’s exam scheduling as part of his Master’s Thesis.

After his Master's program, Djordje moved to Switzerland to complete his Ph.D. within the application of AI to evolutionary biology. Currently, he is undertaking his Postdoc at the IT University of Copenhagen in connection with an international DARPA project concerning self-driving vehicle safety. At the IT University he has managed two courses "Modern AI" and "Applied AI Summer Course" on the MSc Program in Games.


The course is designed for professionals with the following functions:

  • Executives with strategy-level responsibilities, such as Team Leaders, CIO's, IT Managers and Innovation Managers
  • Tech leaders for tech adoption, experts and specialists with influence and/or in the Enterprise Architecture field


Time and place:
The course will take place at the IT University on 28 and 29 October 2021.

The next course this Fall will run 18 - 19 November 2021.

Admission requirements
There are no admission requirements.

10.000 kr. The price does not include value added tax.

The course is taught in Danish. There is no exam but you will be awarded a certificate upon completion of the course.

Join the webinar What is Artificial Intelligence? on Friday September 3, 2021 for a small sampling of the upcoming course Introduction to Artificial Intelligence.

During the free webinar, ITU-postdoc Djordje Grbic will unfold how businesses and organisations may create value with AI and look into the the ways in which companies and organisations use cutting-edge AI technologies in process optimization, marketing, product development and much more.

Read more about the webinar 'What is Artificial Intelligence?' here