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FTMBA2024 Workshop Machine Learning

Course code
Course type
FT MBA Course
Weekly Hours
FS 2024
Martin Prause
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.

The digital society is characterized by producing and interrelating a large amount of data from various sources. To turn (big) data into meaningful information that can feed business models and create competitive advantages, managers should have a sound understanding of the understanding limits of information extraction and processing techniques. In particular, managers should have a business understanding of Machine Learning, Data Science, and Artificial Intelligence.

Machine Learning is learning from data according to a given task. Nowadays, Machine Learning is the most prominent approach in Data Science and Artificial Intelligence to extract information from images, video, text, and audio, enabling computers to perceive the environment almost like humans perceive it. This opens up completely new areas of business models, technological advancements, and challenges for society.

This course aims to provide the background on Data Science, Artificial Intelligence, and Machine Learning to assess their managerial applicability without the need for extended coding and detailed mathematics.

We will discover and discuss multiple topics such as (but not limited to):

  • Context: Artificial Intelligence versus Machine Learning versus Data Science
  • Theory: Classification, Clustering, Prediction, Recommendation Algorithms
  • Applications: Natural Language Processing, Visual Cognition, Artificial Art
  • Boundaries: AI Ethics, AI Policies
  • Potential: Latest advancements in the field of AI

You should take this course if you are interested in

  • Understanding the interplay of Data Science, Machine Learning (ML), and Artificial Intelligence (AI)
  • Obtaining an overview of the data value chain, the process of Data Science in general, and its consequences on businesses
  • Looking behind the scene of AI and understanding how it works, what are its potentials, challenges, and limitations
  • Understanding and evaluating the latest advancements in the field of AI and putting them into business perspective
  • Directly use and apply ML/AI tools in a low-code/no-code way for quick experimental outcomes

You shouldn’t take this course, if ...

  • you want a to learn programming in the area of data science and machine learning
  • you are interested in the mathematics behind the scence

Day 1, 27.03.2024


  • Disentangling the terms and fields of application concerning Machine Learning, Data Science, Data Mining, Deep Learning, and Artificial Intelligence
  • Overview of AI algorithms, how they work and what are potential business applications
  • Managing a Machine Learning Project: What are the steps involved, what skills, and infrastructure do you need, how to sell your results
  • Using AI Cloud Services to analyze data on a low code base

Day 2, 28.03.2024


Understanding Neural Network as the most prominant approach in AI for Visual
Cognition and Natural Language Processing / Generation.

  1. Object- and facial recognition; Video analysis
  2. Image and video generation
  3. Text analysis and understanding
  4. Understanding conversational agents
  5. Leveraging and directly implementing the power of conversational agents
    (GPT-4, Bard, Claude)
    a. Daily work and task automation
    b. Data analysis
    c. Coding and learning
  6. Understanding the implications on business and society of AI image-, video- and text-generation
Date Time
Wednesday, 27.03.2024 09:00 - 16:30
Thursday, 28.03.2024 09:00 - 16:30
Learning Goals
    • Understanding the foundations of machine learning and artificial intelligence
    • Managing the Data Science value chain
    • Applying Cloud-based ML/AI tools to solve Data Analysis tasks on a low-code base
    • Understand the technology behand visual cognition and natrual language processing
       Identifying images, videos
       Generating AI-based images and videos
       Leverage the power of conversational agents (GPT, Bard, Claude)
       Implement and use conversational agents for various business applications
       Assess the potential and limitations of AI
Literature:Agrawal, Gans, and Goldfarb (2018), Prediction Machines: the simple economics of artificial intelligence, Harvard Business Press.Ford (2018), Architects of Intelligence: The truth about AI from the people building it, Packt Publishing.Frankish and Ramsey (2014), The Cambridge Handbook of Artificial Intelligence, Cambridge University Press.Pearl & Mackenzie (2018), The Book of Why: The New Science of Cause and Effect, Basic Books.Provost and Fawcett (2013), Data Science fpr Business: What you need to know about data mining and data-analytic thinking, O’Reilly.Shalev-Shwartz, and Ben-David (2014), Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.
Traditional Lecture, Coding exercises, Follow-me-through-the-code
None, participation is sufficient
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