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5009689: C19 Advanced Machine Learning

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Semester:WS 20/21
Type:Module
ECTS-Credits:3.0
Scheduled in semester:3
Semester Hours per Week / Contact Hours:30.0 L / 22.5 h
Self-directed study time:67.5 h

Module coordination/Lecturers

Curricula

Master's degree programme in Information Systems (01.09.2019)

Description

Advanced Machine Learning covers several advanced topics in the field of machine learning and is concerned with requirements engineering in particular. Students learn to analyse certain types and large amounts of data. The course covers seven primary topics:

  • Requirements engineering for machine learning and business intelligence projects
  • Frequent patterns and association rules
  • Process mining
  • Time series analysis
  • Anomaly detection
  • Fundamentals of computational efficiency and distributed and parallel computing
  • Hadoop ecosystems, with a focus on Spark and MLlib

Learning Outcomes

After successful completion of the course, students will:

  • have deepened their understanding in the field of machine learning and acquired a larger set of machine-learning techniques
  • understand the challenges and solutions of processing large amounts of data
  • be able to gather requirements for projects in the field of machine learning and business intelligence

Qualifications

Lectures Method

  • The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
  • The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

Admission Requirements

  • Though not mandatory, students should have attended the second-semester course Data Science.

Literature

Compulsory reading:

  • Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, The Netherlands: Elsevier.
  • Aggarwal, C.C. (2015). Data Mining: The Textbook. Heidelberg, Germany: Springer.