Information on individual educational components (ECTS-Course descriptions) per semester

Elective: Artificial Intelligence

Degree programme Computer Science - Digital Innovation
Subject area Engineering Technology
Type of degree Bachelor
Summer Semester 2024
Course unit title Elective: Artificial Intelligence
Course unit code 083121160102
Language of instruction English
Type of course unit (compulsory, optional) Elective
Teaching hours per week 3
Year of study 2024
Level of the course / module according to the curriculum
Number of ECTS credits allocated 5
Name of lecturer(s) Steffen FINCK, Sebastian HEGENBART, David SAUERWEIN
Requirements and Prerequisites


Course content
  • Introduction to the History of Artificial Intelligence (AI)
  • Taxonomy of the fields of research of AI (for example Machine Learning, Natural Language Processing)
  • Fields of application of AI methods
  • General concepts of data mining (e.g., scale levels, training / test data)
  • Cluster analysis and regression / classification including appropriate algorithms (e.g., K-Means, Decision Trees, Naive Bayes ...)
  • Concepts and methods of natural language processing (e.g., text segmentation, determination word functions ...)
  • Other terms: No-free-lunch theorem, cold start problem generalization, overfitting. underfitting, transfer learnings
Learning outcomes

Artificial intelligence was established in the mid-19th century as a research field of computer science and is considered one of the drivers of the digital revolution. Weak artificial intelligence, which is the focus of this course, has the goal of helping people with specific application problems. Applications are e.g. sata mining, text recognition, speech recognition or computer algebra systems, which have already demonstrated their usefulness in our daily lives. Among others, different methods of machine learning, e.g. classification or clustering procedure.

Theoretical and methodological know-how (T/M): 

  • Students know the basic concepts in the context of Artificial Intelligence (AI) and are able to identify and describe possible applications of AI methods.
  • Students know the basic concepts and procedures in the context of machine learning (ML) and its core elements (supervised, unsupervised, reinforcement).
  • Students are able to explain various ML methods (e.g., Decision Trees, Support Vector Machines).
  • Students select and implement appropriate methods (based on the requirements of a task and the characteristics of the procedures).
  • Students are able to explain and use procedures that are used for data mining (eg regression, ML procedures ...).
  • Students are able to describe the need for method evaluation (no-free-lunch theorem) and apply appropriate procedures (e.g., cross-validation, receiver-operating-characteristic curves) and interpret the results.
  • Students are able to use a programming language / software (eg Python, R, WEKA) to solve simple ML tasks and interpret the results.

In addition, social and communicative skills (S/C) such as motivation and reliability as well as self-competences (S) such as learning and motivation, decision-making, responsibility and perseveranceare are trained.

Planned learning activities and teaching methods

Integrated course: 3 THW.

Lectures and practical exercises, which have to be implemented. 

Assessment methods and criteria
  • Submission and presentation of projects (100 %


Recommended or required reading
  • Barr, Avron; Feigenbau, Edward A (1979): Handbook of Artificial Intelligence. Online im Internet:
    URL: (Zugriff am 10.09.2018)
  • Flach, Peter (2007): Simply Logical: Intelligent Reasoning by Example. Online im Internet:
    URL: (Zugriff am 10.09.2018)
  • Gorunescu, Florin (2011): Data Mining: Concepts, Models and Techniques. Springer Science & Business Media.
  • Hal, Daumé (2012): A Course in Machine Learning. Online im Internet: URL: (Zugriff am 10.09.2018)
  • Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2017): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. 2nd ed. 2009, Corr. 9th printing 2017 edition. New York, NY: Springer.
  • James, Gareth u.a. (2017): An Introduction to Statistical Learning: with Applications in R. 1st ed. 2013, Corr. 7th printing 2017. New York: Springer.
  • Nilsson, Nils J. (2009): The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge: Cambridge University Press. Online im Internet: DOI: 10.1017/CBO9780511819346 (Zugriff am: 11.06.2018).
  • Poole, David L; Mackworth, Alan K (2017): Artificial Intelligence: Foundations of Computational Agents,  2nd Edition. Online im Internet: URL: (Zugriff am: 11.06.2018).
  • Python Software Foundation (o. J.): python. Online im Internet: URL: (Zugriff am: 21.05.2018).
  • R Foundation (o. J.): R. Online im Internet: URL: (Zugriff am: 21.05.2018).
  • Russell, Stuart; Norvig, Peter (2018): Artificial Intelligence: A Modern Approach. Online im Internet: URL: (Zugriff am: 27.08.2018).
  • Technology Partners (2017): SWC. What is Artificial Intelligence, Really? Behind the Buzzword. Online im Internet:
    URL: (Zugriff am: 13.06.2018).
Mode of delivery (face-to-face, distance learning)

In-class lecture: Compulsory attendance in the practice session.