ST-LE-2024-17
Curriculum: Starter TrackMachine learning basics
Motivation
Have you ever considered incorporating machine learning techniques into your research but felt deterred by its perceived complexity? The success of machine learning in quite complicated tasks might create the misconception that it is exclusively for experts. At the same time, machine learning is sometimes perceived as a magic tool, seemingly capable of solving any task. In this case the inherent limitations of machine learning are missed because an understanding of its fundamentals is lacking. However, most machine learning techniques are based on principles that can be explained on many levels of complexity.
Learning Contents
In this talk we are going to explain the general concepts of machine learning on a basic level. Instead of delving into the intricacies of specific techniques, we'll shed light on the distinctions and commonalities among various algorithms. Specifically, we'll explore the principles of supervised learning starting from linear regression. With regard to unsupervised learning we will discuss how clustering algorithm differ in their criteria to identify clusters in data. Additionally, we may touch upon dimensionality reduction techniques.
Learning outcomes
The goal is to empower you with a foundational understanding of machine learning, accompanied by an awareness of its limitations, encouraging you to explore its potential without feeling overwhelmed by complexity.
Prior knowledge and requirements
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Further reading
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Motivation
Have you ever considered incorporating machine learning techniques into your research but felt deterred by its perceived complexity? The success of machine learning in quite complicated tasks might create the misconception that it is exclusively for experts. At the same time, machine learning is sometimes perceived as a magic tool, seemingly capable of solving any task. In this case the inherent limitations of machine learning are missed because an understanding of its fundamentals is lacking. However, most machine learning techniques are based on principles that can be explained on many levels of complexity.
Learning Contents
In this talk we are going to explain the general concepts of machine learning on a basic level. Instead of delving into the intricacies of specific techniques, we'll shed light on the distinctions and commonalities among various algorithms. Specifically, we'll explore the principles of supervised learning starting from linear regression. With regard to unsupervised learning we will discuss how clustering algorithm differ in their criteria to identify clusters in data. Additionally, we may touch upon dimensionality reduction techniques.
Learning outcomes
The goal is to empower you with a foundational understanding of machine learning, accompanied by an awareness of its limitations, encouraging you to explore its potential without feeling overwhelmed by complexity.
Prior knowledge and requirements
---
Further reading
---
When?
April 30, 2024, 10:00 AM - 12:00 PM
Where?
UNICOM 2
Haus 2 (Oxford)
Small seminar room, 4.3250 (3rd floor)
Mary-Somerville-Str. 2
28359 Bremen
and
Online via Zoom
Registration closed
Cooperating in U Bremen Research Alliance:
