Herr Hendric Voß: Teaching

Weekly schedule Contact

All courses for recent semesters:

SoSe 2026

Reference no. Teaching staff Topic Type Dates My eKVV
392143 Kopp, Voß   Applied Cognitive Computing: Advancing Reinforcement Learning Agents through Cognitive Mechanisms
Limited number of participants: 70 Course taught in English
S Mon 12-14 in CITEC 2.015 [13.04.-24.07.2026]

WiSe 2025/2026

Reference no. Teaching staff Topic Type Dates My eKVV
392101 Kopp, Voß   Cognitive Computing: Reasoning and Decision-Making Under Uncertainty Course taught in English V Fri 10-12 in CITEC [13.10.2025-06.02.2026]
392101 Kopp, Voß   Klausur: Cognitive Computing: Reasoning and Decision-Making Under Uncertainty - 1. Termin
Registration via the electronic course catalogue (eKVV) by 2/12/26
Kl
392101 Kopp, Voß   Klausur: Cognitive Computing: Reasoning and Decision-Making Under Uncertainty - 2. Termin
Registration via the electronic course catalogue (eKVV) by 3/13/26
Kl
392102 Kopp, Voß   Cognitive Computing: Reasoning and Decision-Making Under Uncertainty Course taught in English Ü Wed 14-16 (every two weeks) in CITEC [13.10.2025-06.02.2026]
392136 Robrecht, Voß   Hot Topics in Interactive Cognitive Systems Course taught in English S Tue 10-12 (every two weeks) in CITEC 2.015 [21.10.2025-06.02.2026]

SoSe 2025

Reference no. Teaching staff Topic Type Dates My eKVV
392142 Kopp, Österdiekhoff, Voß   Applied Cognitive Computing: Advancing Reinforcement Learning Agents through Cognitive Mechanisms
Limited number of participants: 70 Electronic course catalogue (eKVV) participant management Course taught in English
S Mon 14-16 in V2-205 [07.04.-18.07.2025]
Tue 08-10 in CITEC 2.015 [07.04.-18.07.2025]
392143 Kopp, Österdiekhoff, Voß   Tutorial: Applied Cognitive Computing: Advancing Reinforcement Learning Agents through Cognitive Mechanisms
Limited number of participants: 70 Electronic course catalogue (eKVV) participant management Course taught in English
Ü Mon 12-14 in CITEC 2.015 [07.04.-18.07.2025]
392241 Voß   Applied Cognitive Computing: Deep reinforcement learning with bounded rationality S

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