Winter 2020/2021

Bachelor / Undergraduate

Projektgruppen

Wissensentdeckung und Maschinelles Lernen
Bachelor BA-INF 051

In dieser Projektgruppe werden grundlegende Algorithmen aus den Bereichen Wissensentdeckung und Data Mining erarbeitet, diskutiert, implementiert und empirisch evaluiert.

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke, Florian Seiffarth, Till Schulz

Contact: Till Schulz

Supervisors: tba

Details

Time:    Vorbesprechung am 03.11.2020  14:00-16:00

Place:   online (siehe unten)

Participants: Maximal 6

In der Projektgruppe werden grundlegende Algorithmen aus den Bereichen Maschinelles Lernen, Wissensentdeckung und Data Mining vorgestellt. Die Aufgabe der Studierenden ist es, in Kleingruppen jeweils einen Algorithmus zu erarbeiten und einen wissenschaftlichen Vortrag darüber zu halten. Im Anschluss soll der Algorithmus implementiert und evaluiert werden. Neben einem Abschlussvortrag soll eine schriftliche Ausarbeitung erstellt werden.

Kursmaterialien

• Sind bei eCampus verfügbar.
• Wie man Zugriff zu eCampus erhält, erfahren Sie hier.
  

Wichtige Termine

• Vorbesprechung: 03.11.2020  14:00-16:00

• Bitte registrieren Sie sich im ecampus Kurs bis zum 03.11.2020, um an der Vorbesprechung teilzunehmen. Wir werden dort einen Link und alle weiteren Informationen bereitstellen. 

Master / Graduate

Lectures/Vorlesungen

Intelligent Learning and Analysis Systems: Machine Learning
Master: MA-INF 4111

Introductory course to machine learning.

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath

Exercises: Eike Stadtländer

Contact: Dr. Tamas Horvath

Details

Start date: 2020-11-06

Time:  Fr 12:15-13:45 s.t.

Place: online

Exercise time:   Fr 14:00-15:30 s.t.

Exercise place: online

  Fouad Alkhoury

  Alexander Zorn

Description: 

With more and more data available for analysis and decision making - from web documents and digital media to sensory data from cameras, microphones, and ubiquitous devices - it becomes increasingly more important to understand how such large volumes of data can be analyzed by computers and used as the basis for new intelligent services, for decision making, and for making computers learn from experience. In companies around the world, from retail and banks all the way to Google, intelligent learning and analysis techniques are used to improve business decisions. Likewise, in science, important discoveries are made easier by automated learning methods, and games and other artifacts are being made adaptive with learning technology.

Within the intelligent systems track of the computer science Master's program, intelligent learning and analysis systems are one of the two major topics. This module (Machine Learning) is one of the two modules that are offered as an introduction for master's students to Intelligent Learning and Analysis Systems. The other is the Data Mining module taught in the summer semester. Both modules can be selected in either order, and you may choose to attend one or both of them. For a complete introduction to the topic, it is recommended to attend both modules.

In the Machine Learning module in particular, we will give a practically oriented introduction into the most popular methods from Machine Learning as a subfield of Intelligent Learning and Analysis Systems. We will get to know decision tree methods, instance-based learning, artificial neural networks, probabilistic learning, regression methods, kernel methods and support vector machines, and reinforcement learning for intelligent agents. This will be complemented with lectures on the most important approaches within computational learning theory. Within the exercises, it is possible to try out the most important methods and popular Machine Learning systems.

Course Organization & Materials

• Materials are available via eCampus. You must enroll in eCampus on or before November 5 (Read how to access eCampus)
• You are allowed to form excercise groups (of at least four and at most five students) if you know other students participating in this course. (We don't release the list of participants for privacy reasons.) In this case, please send an email with subject "ILAS2021: Excercise Groups" to Fouad Alkhoury, Alexander Zorn, and Dr. Tamas Horvath with the name (surname, given name) and email addresses of all group members before 2020-11-05, 12:00 o'clock. Otherwise, you will be added to some group at random and informed about your group via email on 2020-11-05. Please check your emails on 2020-11-05 evening for your exercise class and tutor.
  

Important Dates

• exam (1st try): tba
  
• exam (2nd try): tba
  

Learning from Non-Standard Data
Master: MA-INF 4303

This lecture introduces a selection of machine learning and data mining algorithms developed for graphs and relational structures.

Lecturer: Dr. Tamas Horvath

Contact: Dr. Tamas Horvath

Details

Start date: 2020-11-04

Time:  Wed 14:15-15:45

Place: online

Exercise time:   Wed 16:00-17:30

Exercise place: online

Tutor: Richard Palme

Description: 

Traditional machine learning and data mining algorithms are resorted to data that can be represented by a single table of fixed width; the rows and the columns correspond to objects and object attributes, respectively. This assumption turns out to be quite restrictive in numerous practical applications involving structured data, such as graphs or relational structures. The lecture will cover the basics of learning and mining graph structured data and relational structures. We will present various algorithms for this setting and analyse their computational properties. We will also discuss some interesting applications in bioinformatics, computational chemistry, and natural language processing.

Course Materials

• Materials are available via eCampus. You must enroll in eCampus on or before 2020-11-04 (Read how to access eCampus)
• You are allowed to form excercise groups (of at least four and at most five students) if you know other students participating in this course. (We don't release the list of participants for privacy reasons.) In this case, please send an email with subject "LNSD2021: Excercise Groups" to Richard Palme and Dr. Tamas Horvath with the name (surname, given name) and email addresses of all group members before 2020-11-04, 23:59 o'clock. Otherwise, you will be added to some group at random and informed about your group via email on 2020-11-05. Please check your emails on 2020-11-05 evening for your exercise group.

Important Dates

• exam (1st try): tba
  
• exam (2nd try): tba

Pattern Recognition (I)
Master: MA-INF 4229

Description:

The following topics are covered in this lecture:

• uni-/ multi-variate (non-)linear Regression

• (non-)linear Classification

• Bayesian Methods

• k-Nearest Neighbors. kD-trees

• Bias-Variance-Dilemma

• Hoeffding inequality, Vapnik-Chervonenkis inequality & dimension

• k-Means in hard and soft forms

• Gaussian Mixture Models

• EM-Algorithm

• Linear/ Quadratic/ Fisher Discriminant Analysis

• Linear Dimensionality Reduction

• Lagrange Multipliers and Duality, Karush-Kuhn-Tucker Conditions

• Gradient Methods

• Constrained Optimization

• Support Vector Machine

• Kernels

• Perceptrons, Feed Forward/ Recurrent Neural Networks and their training

• Ressource-consumption of ML Systems

Seminars/Seminare

Principles of Data Mining and Learning Algorithms
Master: MA-INF 4209

(New: Format has changed!) In this seminar we will read several selected topics concerning machine learning and data mining theory and applications from the deep learning book by Goodfellow, Bengio and Courville. We will meet every two weeks via BigBlueButton summarize the topic from two weeks before and present a new topic.

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, Dr. Pascal Welke

Contact: Florian Seiffarth

Prerequisites: MA-INF 4111 / MA-INF 4112 highly recommended

Details

Time:  (see below)

Place: online

Participants: Max. 14

Course Materials

• Are available via eCampus.
• Read how to access eCampus.
  

Important Dates

• preliminary meeting: Monday, 26.10.2020, 10 - 12 (mandatory), ZoomLink to the meeting.

• seminar meetings: every two weeks, exact day and time tba.

• seminar writeup deadline: tba.

Labs/Praktika

Lab Development and Application of Data Mining and Learning Systems:
Data Science and Big Data
Master: MA-INF 4306

In this lab, machine learning and data mining techniques are implemented and used in a wide range of applications.

Lecturers: Prof. Dr. Stefan Wrobel, Dr. Tamas Horvath, PD Dr. Michael Mock, Dr. Pascal Welke

Contact: Dr. Pascal Welke

Supervisors: Michael Mock, Florian Seiffarth, Daniel Trabold, Dorina Weichert, Pascal Welke

Prerequisites: MA-INF 4212 highly recommended

Details

Time:  (see below)

Place: online

Participants: Max. 6

Course Materials

• Are available via eCampus.
• Read how to access eCampus

Important Dates