General Information on the Lecture, Term SS 11:
| EDV Nr. | 38355 |
| Date: | Mo 14.15h-17.30h |
| Room: | 133 |
| SWS/ECTS: | 4/6 |
Announcements:
Intersection with other lectures: The contents of the lecture are described below. Please note that the intersection of this lecture and the MIB-lecture Einführung in die künstliche Intelligenz is kept as low as possible.
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
- Bayesian Learning Theory
- Neuronal Networks
- Support Vector Machines and Kernel Methods
- Learning Association Rules
- Cluster Analysis
- Feature Selection and Feature Extraction
- Learning with Sequential Data / Time-Series Prediction
- Local Modells
- 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.