Welcome everybody, my name is Andreas Maier and I am teaching this semester pattern recognition

at Friedrich-Alexander-Universität Erlangen Nuremberg.

So we are teaching this class as an advanced class for machine learning.

We will focus on classical methods of machine learning.

It will involve quite a bit of math and we aim to link math, statistics, probability

theory together with machine learning and we will talk about linear methods up to Kernel

methods that will involve high dimensional spaces.

So I think the background that you get in this class is crucial for understanding of

course classical machine learning but also for methods of deep learning.

So all that you see here is being published under a creative comments for a zero license.

So you are welcome to reuse any of the material that we present here and we will also publish

all of these videos on our own system which is called FAU TV and as well as on YouTube.

So I will post also the links in the description of this video.

So this would be the right point to subscribe to those videos because then you will see

all of the videos that we will publish throughout the entire semester.

So with that being said I think we are going ahead to some exciting lecture over the next

semester.

So looking forward to demonstrating pattern recognition to you.

We want to start today by talking about the introduction.

So pattern recognition in Erlangen has actually quite some history.

The pattern recognition lab has been founded by Professor Niemann, who you can see here

on the left hand side.

The lab was then later continued by Professor Hornegger who is now the president of the

university.

You can see that quite a bit of what we are demonstrating here in this class goes back,

very back to the roots, to the class that Professor Niemann was already teaching.

Then there was a major re-haul of the entire lecture that was done by Professor Hornegger.

The slides as you can see here in this final presentation form have been created by Stefan

Steidl.

So he did a lot of the contributions that you see here, a lot of the different figures

and animations that you will see in the next couple of slides have been created by him.

Dr. Steidl unfortunately passed in 2018.

Stefan Steidl has been an amazing scholar, a dedicated researcher and a very good friend.

So I am very happy that I can share the slides created by him with you today in this video

as well as for the entire lecture.

So let's talk a bit about the topic of pattern recognition.

This is the classical pattern recognition pipeline.

So you see that we start typically by recording some signal, it could be an image or a speech

signal that is then pre-processed, which means that the signal is essentially preserved in

its original shape.

So it can be played back if it's an audio signal after the pre-processing.

If it's an image signal, you can still look at the image after pre-processing.

And after that, we perform feature extraction and the feature extraction is used to create

meaningful numbers out of the signals and these signals are then used in the classification

stage such that they can be assigned to an abstract class omega kappa.

This entire pattern recognition system, we essentially split up into two lectures.

The first one is introduction to pattern recognition.

So here we talk about the entire feature extraction, typical image and speech processing features

as well as some simple classifiers such that you can build your own classification systems.

The class that you're listening to today is looking into classification and the training