Components of Machine Learning
Event Information
About this Event
Description:
Machine learning combines three components: data, model (hypothesis space) and loss. Machine learning methods are computationally efficient implementations of the scientific principle. This principle amounts to continously learning or adjusting a model based on minimising the loss incurred by comparing model predictions with observed data. Using the three-component view on machine learning provides a unifying framework for a wide range of methods such as linear regression or deep reinforcement learning.
Guest Speaker:
Prof. Alexander Jung
Assistant Professor
Aalto University, Finland
Chair of IEEE Finland