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0000001899 21S 6SWS PR Advanced Practical Course - Machine Learning for Information Systems Students (IN2106, IN2128, IN212812)   Hilfe Logo

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Advanced Practical Course - Machine Learning for Information Systems Students (IN2106, IN2128, IN212812) 
0000001899
practical training
6
Summer semester 2021
Informatics 17 - Chair of Information Systems and Business Process Management (Prof. Rinderle-Ma)
(Contact information)
Details
Allocations: 1 
Angaben zur Abhaltung
Artificial intelligence and machine learning are the most growing topics of our time. Self-learning algorithms, trained systems and semi-autonomous data evaluation enable companies to benefit from their company data like never before.

The steadily increasing data growth combined with the ever shorter becoming time for software product releases in particular requires ever more effective and efficient data analyzes. Learning algorithms are already influencing our working world and our leisure time.

In this praktikum we want to deal with the basics of machine learning. In addition, we want to practice an entire machine learning pipeline and carry out our own short ML project based on practically relevant problems.

If you have any questions about the praktikum, do not hesitate to write an e-mail to the organizers of this praktikum.

Note: Registration takes place via the new matching system. You can find information on this at: http://docmatching.in.tum.de/.

Additionally, a form must also be filled out. You can find it at:
https://forms.gle/jj9MmQ9isd9BnfKW8
The form is open from 27th January to 16th February 2021.
- Basic programming skills (ideally Python)
- Mathematical / statistical basics
- Interest in machine learning
- Basic knowledge of Python Data Science Stack (Numpy, Pandas, Scikit-learn)
- Understand an ML pipeline
- Implementation of your own ML project
English
workshop
9 weeks 4h every two weeks in 2x 2h hour blocks with homework

5 weeks of project work
Details
Für die Anmeldung zur Teilnahme müssen Sie sich in TUMonline als Studierende*r identifizieren.
Zusatzinformationen
Books:
- Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".

- Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media.

- VanderPlas, J. (2016).Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.".

Papers:
- Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS quarterly, 75-105.
- Hevner, A. R. (2007). A three cycle view of design science research. Scandinavian journal of information systems, 19(2), 4.

Further Resources:
- Pandas User Guide, https://pandas.pydata.org/docs/user_guide/index.html
Online information
e-learning course (moodle)
If you have any questions, please contact Mr. Simon Fuchs (s.t.fuchs@tum.de) or Mr. Omar Shouman (o.shouman@tum.de).

Since the corona situation in the coming summer semester 2021 is currently not foreseeable and the organizers want planning security, the course will be held online via Zoom.

Please fill out the following form. You can find it under
https://forms.gle/jj9MmQ9isd9BnfKW8
The form is available in the period fromt 27th January to 16th February 2021.