XAI: Explainable AI explained
This workshop is dedicated to managers that want to learn how to interpret the decisions made by machine learning models.
In the days where we have autonomous cars, drones, and automated medical diagnostics, we want to learn more about how to interpret the decisions made by the machine learning models. Having such information we are able to debug the models and retrain it in the most efficient way.
- We explain the difference between white and black box models, the taxonomy of explainable models and approaches to XAI. Knowing XAI methods is especially useful in any regulated company.
- We go through the basic methods like the regression methods, decision trees, ensemble methods, and end with more complex methods based on neural networks. In each example, we use a different data set for each example.
- Finally, we show how to use model agnostic methods to interpret it and the complexity of the interpretability of many neural networks.
The workshop is free of charge. We also invite you for lunch and tasty coffee!
Trainer: Karol Przystalski
Karol Przystalski is CTO and founder of Codete . He obtained a Ph.D in Computer Science from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and was a research assistant at Jagiellonian University in Cracow. His role at Codete is focused on leading and mentoring teams. The company has built a research lab that is working on machine learning methods and big data solutions in specialty areas such as pattern recognition and HDP.