Hyperparameters are important for machine learning algorithms since they directly control the behaviours of training algorithms and have a significant effect on the performance of machine learning models. Bayesian optimization is an optimization framework for the global optimization of expensive Blackbox functions, which recently gained traction in Hyperparameter optimization for machine learning algorithms. In this talk, Bayesian optimization as a method to optimize hyperparameter in Machine Learning models is reviewed. First, we will consider the traditional Bayesian optimization method, then we will consider new suggested methods, which are based on Bayesian Optimization and supposed to be more efficient than the traditional Bayesian optimization method.

How to join online

The talk is held online via Zoom. You can join with the following link:
https://uni-kl-de.zoom.us/j/62521592603?pwd=VktnbVlrWHhiVmxQTzNWQlkxSy9WZz09

Referent: Tahmineh Zakizadeh Fallahabadi, Aon Solution Germany GmbH, Wiesbaden

Zeit: 12:00 Uhr

Ort: Hybrid (Room 32-349 and via Zoom)