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