The Conference for Machine Learning Innovation

Christoph Stockhammer

Christoph Stockhammer
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Register until October 21:
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✓ 10% team discount
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Join the ML Revolution!
Register until September 23:
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10 % Team Discount
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Join the ML Revolution!
Register until September 23:
✓ PS Classic or C64 Mini for free
✓ Save up to €310
10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until the conference starts:
✓ 2-in-1 conference special
✓ 10 % Team Discount
Register Now

Christoph Stockhammer

The MathWorks GmbH

Christoph Stockhammer holds a M.Sc. degree in Mathematics from the Technical University Munich with an emphasis on optimization.
He joined The Mathworks in 2012. His focus areas include Mathematics and data analytics, Machine & Deep Learning as well as the integration of MATLAB software components in other programming languages and environments.

This Speaker belongs to the Dieser Speaker gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

This Speaker belongs to the Dieser Speaker gehört zum Programm vom MunichMunich program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

Take me to the full program of Zum vollständigen Programm von Munich Munich .

This Speaker Dieser Speaker belongs to the gehört zum Programm von MunichMunich program. Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

All talks by Christoph Stockhammer

No current talks available.

All talks by Christoph Stockhammer from other editions

munich 2021 munich 2021 -

Leveraging Reinforcement Learning for Improvement of Traditional Controllers for a Vehicle Model – A Case Study



All talks by Christoph Stockhammer

No current talks available.

All talks by Christoph Stockhammer from other editions

munich 2021 munich 2021 -

Leveraging Reinforcement Learning for Improvement of Traditional Controllers for a Vehicle Model – A Case Study



Behind the Tracks