Data & AI Literacy Hub

Machine Learning Applications - ML for Engineering

Prof. Kristian Kersting - TU Darmstadt

Overview

Duration	~ 5 hours
Difficulty
Lessons	8

machine learning engineering

For

Domain experts with existing mathematical knowledge
CS students

Other Prerequisites

Solid mathematical knowledge

Outcomes

Understanding of machine learning concepts and methods

In this lecture series, you will gain application-oriented insights into the basics of machine learning. It covers relevant areas of statistics, data mining and algorithm development. Based on the presented application cases, the students learn to acquire interesting data from an engineering point of view, to filter the data, to extract relevant features, and to build models for diagnosis and prognosis using machine learning methods.

Introduction Linear Models Evaluation SVMs & Unsupervised Learning SVMs & Unsupervised Learning (Supplementary) Decision Tree Learning Deep Learning illustrated using CNNs Deep Learning illustrated using CNNs (Supplementary)