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Courses in winter term 2011/2012 / MSc /BSc project Artificial Intelligence / Machine Learning

Artificial Iintelligence and machine learning are concerned with automation of intelligent behavior.

Main techniques in Artificial Intelligence can be divided into the following groups: "Searching" , "Planing" , "Optimization Techniques” , "Reasoning" and "Approximation Methods". Artificial intelligence is also used in applications, such as search engines, expert systems, the analysis and forecast of stock prices and handwriting or speech recognition.

Although many tedious tasks can be automated by modelling the behavior of a (computer) system manually, many problems require that a system can adapt its reponses based on feedback on former actions, i.e., learn how to act in a better way in the future. Other tasks are just too large-scale for humans to overview, so help from computers is needed.

Machine Learning (also known as Data Mining, Pattern Recognition, Data Analysis, and Classification) is a research area at the intersection of computer science, artificial intelligence, mathematics and statistics, that addresses these problems. It covers general methods and techniques that then can be applied to a vast set of applications such as predicting customer behavior, steering a robot, detect spam, and predict the folding of a protein, to name just a few.

In this course we provide different practical topics from the area of data mining and machine learning, the task is to design and implement an application. This application should be applied on data from different domains (provided by us).

The project allows students to gain practical knowledge and capabilities in the usage of data mining and machine learning algorithms.

  • Each topic is intended for a small group of 3-4 students.
  • Software should be written in Java or C++.
  • Each topic consists of a tool and its proof-of-concept application in an example domain.
  • Groups can start immediately.
  • Each group is supposed to give at least two presentations:
    • a first presentation about ongoing work, showing a first implementation and commenting on problems (around mid term),
    • a final presentation of the whole work (end of term).
  • You can register for topics from now on via email.
  • For topics please contact Rasoul Karimi
Preliminaries: This course is especially suitable for students who attended either Machine Learning or Artificial Intelligenc or Image Processing lectures, or who intendt to attend these lectures in the future.

Instructor: Rasoul Karimi

Time: Mon 14-18 c.t.
Location: B25 Spl
Begin: 31/10/2011
Assignment: Informtics / AI+ML (MSc / BSc)
Modul- Handbuch:MHB
Last Lecture:here