General Information on the Lecture, Term SS 10:

EDV Nr.  38355
Date:  Mo 14.15h-17.30h
Room:  136
SWS/ECTS:  4/6

Announcements:

March, 15th 2010: First lesson of the term

Content

The science of Machine Learning seeks to answer the question

How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?

In contrast to conventional computer systems, adaptive systems that integrate Machine Learning algorithms do not only process data, but try to extract knowledge from the available data and apply this knowlege for self-correction and -optimisation.
Machine Learning can be considered as an intersection of Computer Science and Statistics. Whereas Computer Science has focused primarily on how to manually program computers, Machine Learning focuses on the question of how to get computers to program themselves. Whereas Statistics already deals with the problem of how to infer knowledge out of a given set of data and some modelling assumptions, Machine Learning incorporates additional questions about what computational architectures and algorithms can be used to most effectively capture, store, index, retrieve and merge these data. Machine Learning also applies results and models from neuroscience and psychology
Machine Learning is an essential part of Artificial Intelligence. Data-/Web-Mining and Pattern Recognition can be considered as Machine Learning applications. A more concrete but not complete list of applications is:


  • Robotics
  • Marketbasket analysis, customer analysis and recommender systems
  • Object Recognition (including character- speech- and face recognition) and Computer Vision
  • Search Engines
  • Sentiment Analysis, Opinion Mining
  • Gaming
  • Speech- and ImageCompression
  • ...

The lecture aims to provide the necessary theory as well as insights into the most important applications of Machine Learning. In the excercises students learn to program algorithms and small applications. Python is applied for this programming since it is easy to learn and provides a bunch of helpful packages.

The lecture is structured as follows

  • Introduction, Overview and Categorisation
  • Relations between Machine Learning, Artificial Intelligence and Data Mining
  • Bayesion Learning Theory
  • Decision Trees
  • Neuronal Networks
  • Support Vector Machines
  • Learning Association Rules
  • Cluster Analysis
  • Feature Selection and Feature Extraction
  • PCA-based Face-Regognition
  • Reinforcement Learning

An excerpt of applications, which will be programmed in the exercises:

  • Recommender Systems
  • Document Classification
  • Predictions based on linear- and non-linear regression
  • Character recognition
  • Face recognition
  • Colorspace transformation
  • ...

In the first exercise an introduction into the basic concepts of Python will be provided.