Administrivia

Introduction to AI (I2AI)

Andy Weeger

Neu-Ulm University of Applied Sciences

February 9, 2026

General remarks

Motivation and goals

Artificial Intelligence (AI) is becoming a basic innovation that is developing into a driver of digitalisation and autonomous systems in all areas of life.

• AI will change life, work, the economy and communication in a similar way to the internet.
• The release of openAI’s chatGPT at the end of 2022 was a milestone in the history of AI.
• Since then, most people have realised that AI will soon change the way we learn, work and live.
• But large language models are only one type of AI. There are many other AI types, each with their own applications.

This lecture will give you a clear understanding of different types of AI, what they do, and how they work.

Format of the lecture

This course will use a flipped classroom approach for most lectures: you will prepare the content at home and we will do exercises on site.

The focus of the classes is to briefly introduce major concepts that underpin intelligent systems.

The greatest amount of work is in preparing for and following up lectures in order to become sufficiently familiar with and understand the concepts.

Please prepare your schedule accordingly.

Discussion

What to you expect to learn in this lecture?

Contents

AI is defined here as the study of agents that receive percepts from the environment and perform actions.

In this course, we will

• cover different types of agents (i.e., goal-based and utility-based agents)
• illuminate some concepts and major functions they implement,
• discuss, how to convert these agents into learning agents,
• identify and discuss real life use cases for these agents, and
• have a look at philosophical stances and ethical implications of AI.

Learning objectives

During this course, you should advance your skills in the following areas:

• Understanding of the origins, strengths and limitations of AI
• Basic knowledge of concepts, functions, and use cases of AI
• Basic knowledge of problem-solving algorithms, knowledge representation, probabilistic reasoning, machine learning, and natural language processing
• Capability to apply your knowledge and understanding of AI to different managerial and organizational contexts
• Ability to assess the potential of AI and use it in digital innovations
• Ability to evaluate new information and to question existing assumptions
• Capacity to assess social and ethical implications of AI applications

Supporting literature

Besides the literature cited in the lecture notes, Russel & Norvig (2022) is the recommended accompanying reading for rereading and deepening the concepts

Artificial Intelligence: A Modern Approach, Global Edition, 4/E

Russell / Norvig

ISBN-10: 1292401133 • ISBN-13: 9781292401133

The 3rd edition is available in the libary

Exam

There will be a written exam at the end of the semester.

The exam will

• take place during the examination weeks,
• will last 90 minutes,
• cover all contents discussed in lecture,
• focus on the application of the knowledge gained in the course.

Schedule

It is of importance that you reflect the contents of the session continually, do the exercises and ask questions.

Date	Topic	Preparation
25.03.2026	Administrivia & Introduction	—
02.04.2026	Environments & Agents	Lecture notes
09.04.2026	Search & Planning	Lecture notes
16.04.2026	Knowledge & Inference	Lecture notes
23.04.2026	Modelling of Uncertainty I: Probability Theory	Lecture notes
30.04.2026	Modelling of Uncertainty II: Bayes Net	Lecture notes
07.05.2026	Machine Learning I: Basic Concepts	—
14.05.2026	Public Holiday	Review contents
21.05.2026	Machine Learning II: Decision Trees	Lecture notes
28.05.2026	Witsun Holidays	—
04.06.2026	Witsun Holidays	—
11.06.2026	Machine Learning III: Neural Networks	Article1
18.06.2026	Generative Artificial Intelligence	—
25.06.2026	Governance & Management of AI	—
02.07.2026	Human-AI Interaction	—
09.07.2026	Final Q&A & Exam Preparation	Questions

Table 1: Schedule summer term 2026 (may be subjected to changes)

Concluding remarks

Why don’t we focus on ChatGPT et al. only?

Because I am convinced that as a Master of Science in Digital Innovation Management you should understand some of the theoretical (mathematical) foundations of AI. However, you should also acquire practical skills on your own, e.g. in interacting with GenAI².

Literature

Russel, S., & Norvig, P. (2022). Artificial intelligence: A modern approach. Pearson Education.

Footnotes

1. What Is ChatGPT Doing … and Why Does It Work?

2. Some resources: LearnPrompting — Prompt Engineering Guide, LearnPrompting — Courses, partly free of charge, OpenAI — Prompting Guide, PromptingGuide — Prompt Engineering Guide and Microsoft — First Steps with Copilot