[MUSIK]

the following content has been provided by the University

of Erlangen Nürnberg okay so welcome everybody to the

course pattern recognition this winter semester as usual the

lecture room is crowded that's a usual phenomenon at the beginning of

the lecture and beginning with the lecture tomorrow the number of students

will decrease anyway so we should not panic right now and let's

see what's going to happen if the situation if the situation is

the same in 2 weeks from now we have to think about

other options but for now I think we should should be happy with

this okay pattern recognition is not a simple simple

field I have to say we are very mathematical we

have to do a lot of statistics probability theory this semester so

we will talk a lot about pi and probabilities and

probability density functions pdfs decision theory we will learn about

optimization of convex functions we will learn about different norms

we will learn about perceptrons and a lot of mathematical

concepts that are important for machine learning and just for

the historical remark there are two fields pattern recognition and

machine learning they're very similar these two fields are very similar

and they are considering similar tasks pattern recognition is mostly done

in electrical engineering in in computer science departments this is usually

called machine learning so this lecture could also be called machine learning it

would make no difference in its its contents and let me first introduce myself if you haven't

seen me before my name is Joachim Hornegger I'm here in the CS department working

on pattern recognition medical image-processing and signal processing

signal analysis that's my research topic and we have a very

application oriented team in my lab and the application

field we are mostly considering is medical medicine medical

applications the medical field medical engineering so all the

techniques and technologies we're going to learn this semester

are applied to various situations in industry industrial image processing

signal analysis image analysis medicine and we will see

a lot of different examples and you will learn

within the lecture that these core technologies have very

sound application hello hello it seems to

be a problem I'm sorry

for that let's see what's going to happen tomorrow what's going to

happen tomorrow regarding the slides we have more than 500 slides covering the

topics of the semester we will put all the slides on the web also

the annotated slides you will see that I'm writing a lot and

I'm doing a lot of derivations manually by hand here and you

can also download the annotated flies and if you have the feeling

that I'm delayed for 3 weeks with uploading the annotated file you

should not hesitate to push me and if you write e-mails like

dear professor Hornegger may I kindly ask these are the nice emails

I ignore usually so you have to sharp in what you

want have you thought about the upcoming evaluation sir do you know what's going to

happen if you don't do not upload the annotated files what I will write about

and are you aware that the dean is going to read what I'm writing so