Teaching

We have developed a focused set of courses aimed at addressing the boundary between systems and software engineering, machine learning, computational neuroscience and computer vision. As part the recent revised graduate curriculum for Masters in Computer Science students can take key courses focused on systems science and engineering for intelligence:
1. Systems and Software Engineering I & II – a projects oriented course teaching systems and software engineering practices. Students learn to think holistically and understand how to examine a system from a user perspective, modeling, architecting and design perspective, and from implementation and validation perspectives. This course is complemented by a theory oriented course.
2. Systems Engineering meets Life Sciences I & II – This course focuses 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.
3. Machine learning I & II (Foundations and advanced machine learning) – This course focuses on foundations of machine learning, applied statistics, and the latest advances in machine learning – deep learning, Bayesian learning, etc.
4. Seminar in Advanced topics on Pattern Analysis and Machine Intelligence
5. Seminar in Advanced topics on Systems and Software Engineering

6. Machine Learning Practice –  Practice course in Machine Learning and Intelligent Systems Engineering

Courses Winter Semester 2019/20

Machine Learning I ( Please see Prof. Dr. Bertschinger/Prof. Kaschube’s FIAS page).

Seminar: Pattern Analysis and Machine Intelligence ( Jointly with Prof. Dr. Bertschinger, Prof. Kaschube) http://www.ccc.cs.uni-frankfurt.de/pami-seminar-ws-19-20/

Machine Learning Practice – (with M. Mundt, I. Pliusch)

Systems and Software Engineering I  (Link)

Systems Engineering meets Life Sciences I   (Link)

Courses Summer Semester 2019

Machine Learning II (Jointly with Prof. Dr. Bertschinger).

Link to course page at GU  course page at GU,  Bayesian Methods and ML Foundations – webpage at FIAS

Seminar: Pattern Analysis and Machine Intelligence ( Jointly with Prof. Dr. Bertschinger)   Link to webpage at FIAS

Machine Learning Practice – (with M. Mundt, I. Pliusch) Github link to course material

Courses Winter Semester 2018/2019

Machine Learning I (is offered by Prof. Bertschinger/Prof. Kaschube).

Systems and Software Engineering I (WS 18/19)

Systems Engineering Meets Life Sciences I

Seminar: Pattern Analysis and Machine Intelligence (Jointly with Prof. Bertschinger and Prof. Kaschube) (Link to papers)

Courses Summer Semester 2018

Machine Learning I (is offered by Prof. Bertschinger).

Systems and Software Engineering II (SS 18)

Seminar: Pattern Analysis and Machine Intelligence (see webpage of Prof. Bertschinger at FIAS)

 Pattern Analysis and Machine Intelligence Practice

Courses Winter Semester 2017/2018

Machine Learning I (is offered by Prof. Kaschube and Prof. Bertschinger).

Systems and Software Engineering I

Systems Engineering Meets Life Sciences I

Seminar: Pattern Analysis and Machine Intelligence

 Pattern Analysis and Machine Intelligence Practice

Courses Summer Semester 2017

Machine Learning II 

Systems and Software Engineering II

Systems Engineering Meets Life Sciences II

Seminar: Pattern Analysis and Machine Intelligence

Courses WS 16/17

Machine Learning

Systems and Software Engineering

Systems Engineering Meets Life Sciences

Seminar: Pattern Analysis and Machine Intelligence

Courses SS 16

Machine Learning II

Seminar: Pattern Analysis and Machine Intelligence

Systems and Software Engineering II

Programming I

Introduction to Programming

Courses WS 15/16

Machine Learning

Systems and Software Engineering (I)

Systems Engineering meets Life Sciences

Pattern Analysis and Machine Intelligence (Seminar)

Programming I

Introduction to Programming

Courses WS 14/15

WS 2014/15 – Machine Learning
WS 2014/15 – System Engineering