More talks in the program:
11:45 - 12:45
Clustering is the most prominent example of non-supervised learning, a type of learning where it is not necessary to manually annotate each data point with an expected outcome. This is extremely useful, as getting good annotated data is often the most complicated and time consuming part of an ML project. Clustering can be used as a powerful data exploration and preprocessing technique and also as a means in itself to solve an ML problem.
This talk will give an overview over clustering in general and the different properties of clustering algorithms that are useful when comparing them. It will present various clustering methods, explain how they work and what they can be used for. The talk will cover a lot of ground very quickly and may contain some basic maths. The content will be programming language agnostic, algorithms will be described with free text, pseudocode and diagrams.