Course content:

The lecture is structured in three parts:

In the first part the methods used for architecture design are introduced (system analysis, UML, formal specification of interfaces, software and analysis patterns, and the separation in conceptual and IT-architectures. The second part is dedicated to learning architectures and machine learning methods. The third part presents examples of learning CRM-Architectures.

Workload:

The total workload for this course is approximately 135 hours (4.5 credits):

Time of attendance

• Attending the lecture: 15 x 90min = 22h 30m
• Attending the exercise classes: 7 x 90min = 10h 30m
• Examination: 1h 00m

Self-study

• Preparation and wrap-up of the lecture: 15 x 180min = 45h 00m
• Preparing the exercises: 25h 00m
• Preparation of the examination: 31h 00m

Sum: 135h 00m

Learning Goals:

Students have special knowledge of software architectures and of the methods which are used in their development (Systems analysis, formal methods for the specification of interfaces and algebraic semantic, UML, and, last but not least, the mapping of conceptual architectures to IT architectures.

Students know important architectural patterns and they can – based on their CRM knowledge – combine these patterns for innovative CRM applications.

Assessment:

The assessment consists of a written exam of 1-hour length following §4 (2), 1 of the examination regulation and by submitting written papers as part of the exercise following §4 (2), 3 of the examination regulation.

The course is considered successfully taken if at least 50 out of 100 points are acquired in the written exam. In this case, all additional points (up to 10) from exercise work will be added.

Grade: Minimum points

• 1,0: 95
• 1,3: 90
• 1,7: 85
• 2,0: 80
• 2,3: 75
• 2,7: 70
• 3,0: 65
• 3,3: 60
• 3,7: 55
• 4,0: 50
• 5,0: 0

• P. Clements u. a., Documenting Software Architectures. Views and Beyond. Upper Saddle River: Addison-Wesley, 2011.
• Fowler, Patterns of Enterprise Application Architecture. Amsterdam: Addison-Wesley Longman, 2002.
• S. Russell und P. Norvig, Artificial Intelligence: A Modern Approach, 3. Aufl. Harlow Essex England: Pearson New International Edition, 2014.
• V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.