Vorarlberg University
of Applied Sciences

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

Elective: Artificial Intelligence

Degree programme	Computer Science - Software and Information Engineering
Subject area	Engineering & Technology
Type of degree	Bachelor Full-time Summer Semester 2025
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	2025
Level of the course / module according to the curriculum
Number of ECTS credits allocated	5
Name of lecturer(s)

Requirements and Prerequisites

None

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 %

Comment

None

Internal

Elective: Artificial Intelligence