|| Dipl.-Inf. Christian Osendorfer
|| WS 2005/2006
| Zeit & Ort
|| Di 16:00 - 18:00 MI 03.07.023
|| erfolgreiche Teilnahme am Praktikum
The lab course is over!
We will start with ancient statistical techniques such as Bayes
Linear Discriminant Analysis (LDA)
as well as more recently established methods such as feed forward Neural Networks
and Hidden Markov Models
The area of Machine Learning has grown tremendously over the past 15 years, and
lots of new approaches have been developed in this period.
Some of these (Support Vector Machines
Long Short Term Memory
Independent Component Analysis
) will also be treated in this course.
There will be about 10 assignments during the semester. Each will be discussed in a one hour meeting taking place once every week.
You are supposed to solve the assignments in groups of 2 or 3 people. Each assignment is centered around the understanding
and implementation of one specific machine learning technique.
In order to test your implementations, the assignments will come with data sets from meaningful applications.
The course belongs to Theoretische Informatik and Technische Informatik (this is important only
if you are enrolled in the Diplomstudiengang Informatik
You should be familiar with the contents of Analysis I/II,
Linear Algebra I/II and Probability Theory. See also the next remark!
This course is mainly about implementing machine learning algorithms.
It is not
about learning how to program. You are supposed to
know the basics of programming. You are free
your programming language and environment, i.e. you could use also
There are lots of books on Machine Learning. Yet every assignment is self-contained;
therefore books should not be necessary (but might, of course, be valuable addenda).
A recommended classic