Explainable Artificial Intelligence Explained

  • TYPE: Session DATE: Wednesday, November 25, 2020 TIME: 12:30-12:50

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.
This talk is dedicated to managers, developers and data scientists that want to learn how to interpret the decisions made by machine learning models. 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.

- Understand the goals of XAI
- Know the difference between black and white-box models
- Understand the challenges we face using machine learning


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Speaker:

Obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. CTO and founder of Codete. Leading and mentoring teams of Codete. Working with Fortune500 companies on data science projects. Built a research lab that is working on machine learning methods and big data...


Session Links


Virtual Event

cybernetix.world 2020

Language:
English
Contact person:

Ms. Lauren Zuber
+49 211 23707725
lz@kuppingercole.com
  • Nov 24 - 25, 2020