Rise of AI Academy - Explainable AI

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 training 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. Target Audience: Managers who want to understand the decisions made by machine learning models. Data scientists who have little or no experience with XAI. Python developers who would like to extend their knowledge on machine learning. Developers who want to explain to the managers or other audience how the model she/he created works under the hood. Participants will understand: - the difference between white and black-box machine learning models, - what type of models are easier to explain and why it’s easier to explain such, - what methods are used to explain machine learning models. Participants will be able to use model agnostic methods, interpret machine learning models. Outline What is XAI? Differences between black and white-box models Models that are easy to explain Model-agnostic methods Explaining deep neural networks Online only event! You will receive your log-in data before the event. No previous knowledge required. Webinar language: English