Artificial intelligence and machine learning have long since found their way into large parts of industry. Success has most recently been achieved in autonomous driving, medical image processing or material testing. In future, Artificial Intelligence (AI) will play an even greater role in numerous industries and scenarios. To remain fit for this future, companies have to deal with the basics of AI and machine learning.

These basics are imparted in the workshop on Machine Learning, which bundles the sheer flood of information and offers you a compact overview of theory and practice in machine learning. You receive the hands-on knowledge you need to integrate the enormous potential of artificial intelligence into your product portfolio and value chain.

This workshop is very practice-oriented. Half of it consists of applied exercises on a consistent topic. You will learn the relevant methods and practices around machine learning while focusing on artificial neural networks, the basis for deep learning.

CONTENTS

   Basics

   Introduction and definition of terms (machine learning, artificial intelligence, big data, deep learning, ...)

   Presentation of the technologies used in the workshop, e.g. TensorFlow and Keras

   Landscape of Machine Learning Methods

   Supervised learning

   Unsupervised learning

   Reinforcement learning

   Single-layer neuronal networks (perceptrons)

   Biological motivation

   From biological to artificial neurons

   Learning: optimization, gradient descent

   Classification of multiple classes

   Basic terms & tools

   Loss functions

   Performance metrics

   Data partitioning

   Feature extraction, dimensionality reduction

   Overfitting and countermeasures

   Multilayer neural networks

   Backpropagation

   Deep learning