Teknologiseminar
From Robin
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| 01.02 || Python testbenches in FPGA/ Digital Design course || Yngve Hafting | | 01.02 || Python testbenches in FPGA/ Digital Design course || Yngve Hafting | ||
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| 15.02 || || Mats? | | 15.02 || || Mats? | ||
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| 23.05 || Explaining, interpreting, and decision tuning ML-models built on tabular data || Tom F. Hansen | | 23.05 || Explaining, interpreting, and decision tuning ML-models built on tabular data || Tom F. Hansen | ||
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- | | 30.05 || || | + | | 30.05 || || Kai |
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Revision as of 15:49, 2 February 2024
Meeting structure
Place: ROBIN pause area, 4th floor Ole-Johan Dahls hus / gather town
12:00-12:15 Brief exchange of recent news, plans, updates, etc.
~12:20-12:40 Presentation on: Technology / Own Research / Journal review (see guidelines), time excludes discussion
Whatever content you present, remember that some of those attending have no advance knowledge about your project or work so introduce it in the beginning and stay focused on the main issues/challenges etc. Thus, make your presentation simple and understandable for as many as possible.
Dates and subjects - Spring 2024 – Thursdays 12:15-13:00
5-20 min housekeeping/updates + 20 min talk
When | What | Who |
18.01 | Christmas decorations removal | All present |
25.01 | Towards Sound Innovation Engines Using Pattern-Producing Networks and Audio Graphs [1] | Björn Þór Jónsson |
01.02 | Python testbenches in FPGA/ Digital Design course | Yngve Hafting |
08.02 | ||
15.02 | Mats? | |
22.02 | Vegard? | |
29.02 | Adel? | |
07.03 | Ivar-Kristian? | |
14.03 | Pedro Lucas? | |
21.03 | ||
28.03 | ||
04.04 | Easter Break | -- |
11.04 | ||
18.04 | ||
25.04 | ||
02.05 | ||
09.05 | ||
16.05 | ||
23.05 | Explaining, interpreting, and decision tuning ML-models built on tabular data | Tom F. Hansen |
30.05 | Kai |
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
- SLURM
- R
- Python as alternative to R, incl. matplotlib
- ROS
- Deep learning frameworks and practice
- Unity ML agents