Summary:
|
This half credit course provides an
introduction
to supervised learning
and a variety of classification methods, as well as to
concepts and procedures for evaluating and comparing the algorithms.
At the end of the course you should be able to
- Explain the aims, history, and application fields of
machine learning
- Demonstrate an understanding of statistical and rule
based
methods and their differences
- Discuss the advantages and disadvantages of various
learning algorithms
- Choose appropriate algorithms for a given data set
and
develop a setup for testing the prediction quality of each method
- Validate results by using appropriate validation
methods
|
| Time & place: |
Tuesday, Thursday 15.30 - 17.00, venue TH248 |
| Assessment: |
Two 90 min open book class tests (20 %
each), one
group project (60 %) |
| Required reading:
|
D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), Machine
Learning, Neural and
Statistical Classification.
This book is freely downloadable in PDF format. |
| Recommended reading: |
Margaret H. Durham, Data Mining
|
|
Tom Mitchell, Machine
Learning |