• Introduction to Robotic Process Automation Concepts
• Introduction to the Robot Framework (https://robotframework.org/)
• Simple Cases of Process Automation using the Robot Framework
• Introduction to some basic Machine Learning and Text Mining methods for Process Automation
• Combining Robot Framework and Machine Learning for Robotic Process Automation

• Acquire a basic understanding of the importance of Robotic Process Automation
• Learn about business process automation using software robots
• Understand the integration of the process automation with machine learning
• Understand the basics of robotic process automation via simple examples

According to the examination regulation 80% attendance is intended for a PI.

• Lecture
• Practical Exercises (homework, not graded but discussed as desired by the participants)
• Programming project (homework, presented at final unit and graded)

For the programming project participants select a topic in coordination with the lecturer. The project involves robotic process automation using the robot framework and some very simple machine learning approach as presented in the lecture. The programming project will be similar but not identical to the examples presented in the lecture; it solves the conceptual and technical problems posed by a simple scenario and illustrates the usefulness of the approach. As a software project it of course includes documentation.

Since we only use open source software those students who wish to use their own computer can easily install all packages themselves.

Python coding will be necessary in the examples and in the final programming project, but everything will be explained in the lectures and can be accomplished by participants without prior experience in the Python programming language.

Virtual consultation via Jitsi will be provided in between units for participants who face programming obstacles. Screen sharing will usually solve these problems quickly.

Three short multiple choice quizzes via Learn 50%

Final individual programming project presentation 50%

Grading: 50% of total points = 4, 62% = 3, 74% = 2, 86% = 1

Some experience with Python programming will be helpful but not necessary.

Directly before or after the course or via E-Mail: johann.mitloehner@wu.ac.at