- 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
Syllabus
Title
1118 Applications of Data Science: Robotic Process Automation with Machine Learning
Instructors
ao.Univ.Prof. Dr. Johann Mitlöhner
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/04/24 to 09/16/24
Registration via LPIS
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
| Day | Date | Time | Room |
|---|---|---|---|
| Wednesday | 10/16/24 | 02:30 PM - 06:30 PM | TC.4.18 |
| Wednesday | 10/23/24 | 02:30 PM - 06:30 PM | TC.4.18 |
| Wednesday | 10/30/24 | 02:30 PM - 06:30 PM | TC.5.18 |
| Wednesday | 11/06/24 | 02:30 PM - 06:30 PM | TC.3.06 |
| Wednesday | 11/13/24 | 02:30 PM - 06:30 PM | TC.3.06 |
| Wednesday | 11/20/24 | 02:30 PM - 06:30 PM | TC.5.14 |
- 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
- Lecture
- Short multiple choice quizzes after each lecture via LEARN
- Practical Exercises (homework, not graded but discussed as desired by the participants)
- Programming project, presentation in final unit
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 approaches as presented in the lecture. The programming project will be similar in method but not identical in content 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.
We only use open source software: Python, Jupyter Notebook, and a number of other Python packages. If you can run the pip install manager, then you can install everything we need. All course materials are available online at mitloehner.com. Python coding will be necessary in the examples and in the final programming project.
Short multiple choice quizzes after each unit via Learn 70%
Final individual programming project 30%
Grading: 50% of total points = 4, 62% = 3, 74% = 2, 86% = 1
Please note that grades are assigned strictly as above, without exception: no 'rounding', no extension of deadlines, no additional quizzes or assignments.
The use of AI-based tools like chatGPT for generating the text or code of the final programming project is not allowed.
Successful conclusion of the course 1 of SBWL Data Science.
Please be aware that, for all courses in this SBWL, registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).
Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.
Last edited: 2024-05-18
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