Hasso-Plattner-Institut20 Jahre HPI
Hasso-Plattner-Institut20 Jahre HPI
  
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Machine Learning in BPM (Wintersemester 2019/2020)

Lecturer: Prof. Dr. Mathias Weske (Business Process Technology) , Sven Ihde (Business Process Technology)

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.-30.10.2019
  • Teaching Form: Seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English
  • Maximum number of participants: 12

Programs & Modules

IT-Systems Engineering MA
  • BPET-Konzepte und Methoden
  • BPET-Spezialisierung
  • BPET-Techniken und Werkzeuge
  • OSIS-Konzepte und Methoden
  • OSIS-Spezialisierung
  • OSIS-Techniken und Werkzeuge
  • ITSE-Analyse
  • ITSE-Entwurf
  • ITSE-Konstruktion
  • ITSE-Maintenance
Data Engineering MA

Description

In this seminar, the students will get introduced into general machine learning concepts and how they are applied in the context of Business Process Management.

As this application is still highly discussed in research, the students will be presented current research topics, which are provided by the research members of the Business Process Technologies chair. Each student can then choose from this collection of topics their most favorite one to work on - during the semester in groups of at most 2. During the whole semester a member of the chair will be available to help and guide you for the topic.

Additionally, the students will get an introduction into how to scientifically work, including writing a paper and giving scientific presentations.

 

Topics will be presented on Tuesday, October 15th at 15:15 in room H-2.57. (no lecture on Wednesday, October 16th)

 

Slides will be uploaded to the Materialien folder. If you have no access to it, just message sven.ihde@hpi.de.

Requirements

The lecture is for Master students with an interest in testing and implementing machine learning approaches in Business Process Management use cases.

Literature

Necessary literature will be provided for each topic exclusively.

Examination

Each team of two students is graded by their

  1. Short presentation (15min talk) at the middle of the semester [20%],
  2. Final presentation (20min talk) at the end of the semester [30%],
  3. Demo paper (~12 pages, LNCS style) on the scenario and its implementation [40%], and
  4. Review of two of the demo papers of another group [2 x 5%]. 

Dates

  • October 15th - Kick-off: Presentation of Seminar and Topics
  • October 21th - Deadline for submitting choice of topics
  • October 22th - Introduction into Business Process Management
  • October 23th - Introduction into Artificial Intelligence & Machine Learning
  • October 25th - Notification of topic selection result
  • November 5th - How to present scientifically
  • November 19th - How to write scientifically
  • December 3/4th - Mid-term presentation
  • ...

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