Teknologiseminar
From Robin
(Difference between revisions)
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| 15.10 || Bjørn Ivar || Network quality modelling with ΔQ || Ulysse || Unsupervised Domain Adaptation | | 15.10 || Bjørn Ivar || Network quality modelling with ΔQ || Ulysse || Unsupervised Domain Adaptation | ||
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- | | 05.11 || Tom F. Hansen || Introduction, machine learning for automation in underground construction (research) || | + | | 05.11 || Tom F. Hansen || Introduction, machine learning for automation in underground construction (research) || Yngve || Using digital design to create and test pulse width modulating techniques for three phase motor control |
|- | |- | ||
| 26.11 || Minh || Machine learning for speech mood recognition (research) || Abbas || Dynamic modeling and control of soft manipulators | | 26.11 || Minh || Machine learning for speech mood recognition (research) || Abbas || Dynamic modeling and control of soft manipulators |
Revision as of 08:52, 21 October 2020
Meeting structure
Place: ROBIN pause area, 4th floor Ole-Johan Dahls hus
12:00-12:15 Brief exchange of recent news, plans etc.
12:15-13:00 Technology / Own Research / Journal review (see guidelines), including discussion (20 min each, e.g. 15+5)
Dates and subjects - Fall 2020 – Thursdays 12:00-13:00
5-10 min housekeeping updates + talks/roundtable
When | Who 1 | Content 1: Title and type (tech/res/review) | Who 2 | Content 2: Title and type (tech/res/review) |
17.09 | Frank | Research status and preliminary results/ideas | Andrija | Introduction to research |
15.10 | Bjørn Ivar | Network quality modelling with ΔQ | Ulysse | Unsupervised Domain Adaptation |
05.11 | Tom F. Hansen | Introduction, machine learning for automation in underground construction (research) | Yngve | Using digital design to create and test pulse width modulating techniques for three phase motor control |
26.11 | Minh | Machine learning for speech mood recognition (research) | Abbas | Dynamic modeling and control of soft manipulators |
10.12 | Julian | ?? | Mike | ?? |
Examples of interesting topics for technology introduction
Feel free to add topics to the wishlist!
- Robotics
- Reinforcement learning for robotics
- SLAM
- embodied cognition
- Swarm
- Neural networks
- Recursive NNs and LSTMs
- NEAT / HyperNEAT
- Deep NNs
- Evolutionary algorithms and other types of optimization
- Performance comparison of EAs (statistical tests)
- Differential evolution
- Diversity preservation methods
- Novelty search, MAP-Elites and variants
- Bayesian optimization
- Tools
- R
- Python as alternative to R, incl. matplotlib
- ROS
- Deep learning frameworks and practice