Overview:

This two semester course focuses on emerging interdisciplinary perspectives on ‘Systems Science and Engineering for Intelligence’. We focus on natural systems, human and computer vision, bio-inspired vision system designs, and systems theory required for modeling, analysis, simulation and validation of cognitive vision systems. The emphasis is on abstractions, modeling, and rigorous statistical approaches to performance evaluation. Connections are also made between engineering designs and architectural designs in natural systems. The course draws upon years of systems engineering – involving systems modeling, analysis, and validation of computer vision systems and explores links them to latest viewpoints from Machine Learning, Artificial Intelligence, and Brain Sciences. The core emphasis in the course is that there is a natural correspondence between ( Application Contexts, Tasks, Performance Requirements) and (Hardware and Software Programs and their Parameters).  Paradigms popular for systems design – model-based systems engineering vs data-driven machine learning will be contrasted.  The course material is largely based on dissertations, publications, and online video material.

Learning Objective of the Class: 

The objective of the course is to teach foundations in systems thinking that can be applied to design, analysis, and validation of intelligent systems.  Through case-studies in computer vision the students learn systems modeling, simulation and optimization of intelligent systems.

Relationship to Other Courses:

This course complements other courses:  Systems and Software Engineering I and II,  Machine Learning I and II,  Seminar on Pattern Analysis and Machine Intelligence, Software and Systems Engineering Practice,  and Pattern Analysis and Machine Intelligence Practice.

Link to Lecture Schedule: 

https://qis.server.uni-frankfurt.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=258547&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung

Lecture Material:

Link to lecture slides and references –

http://www.ccc.cs.uni-frankfurt.de/syseng-meets-life-sciences-ws18-lecture-material/ (password provided in class)

Link to past years lecture material  (will be revised according to emphasis this semester.) (password protected, will be provided in class).

Relevant Videos:

Systems Engineering First Lecture – 2012