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Machine Learning Applications - ML for Engineering

Prof. Kristian Kersting - TU Darmstadt

Overview
Duration~ 5 hours
Difficulty
Lessons8
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.

Contents

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)

1: Introduction

All lessons on one page
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