PTMBA2024 Workshop Machine Learning

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

Why should I take this course?

You should take this course if you are interested in

• Understanding the interplay of Data Science, Machine Learning, and Artificial Intelligence
• 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 machine learning algorithms and understanding how these algorithms work, what are their potentials, challenges, and limitations
• Understanding and evaluating the latest advancements in the field of Artificial Intelligence and Machine Learning and putting them into perspective

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
• you expect hands-on exercises or case studies. This is only possible with some programming background, but this course is specifically designed for an audience without such knowledge.

ContentDay 1

• Disentangling the terms and fields of application concerning Machine Learning, Data Science, Data Mining, Deep Learning, and Artificial Intelligence
• Overview of Machine Learning 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

ContentDay 2

• Understanding Neural Network as the most prominant approach in Machine Learning
  for Visual Cognition, Natural Language Processing and Artificial Art (e.g., DeepFakes)
• Potential and Challenges of Visual Cognition, Natural Language Processing and Artificial
  Art covering demonstrations such as (but not limited to):

1. Object and Facial Recognition
2. Object detection in video streams
3. Understanding virtual agents and chatbot creation
4. Question-Answering dialogues
5. Automated analysis of Twitter data
6. Automated text summary creation
7. Fake image creation
8. Automatic coding from natural language
9. ...

• Recent advancements in the field of Artificial Intelligence

Learning Goals

• Understanding the foundations of machine learning and artificial intelligence
• Overview of the machine learning landscape
• Managing a machine learning project
• Understanding visual explorative and explanatory data analysis and storytelling
• Understanding the technology behind Visual Cognition and Natural Language Processing
• Evaluating the business value of Visual Cognition and Natural Language Processing
• Overview of the recent advancements in the field of Artificial Intelligence

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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