Clustering and Classification with Python
The aim of this course is to teach you clustering and classification techniques using Python scientific packages such as SciPy, scikit-learn and Statsmodels.
Instructor: András Aszódi.
Topics
- Hierarchical clustering, k-means clustering.
- Gaussian mixtures, E-M algorithm.
- Quadratic and linear discriminant analysis.
- Feature selection with principal components.
- Cross-validation techniques.
- Logistic regression and its connection with discriminant analysis. Multinomial regression.
Out of scope
"Machine learning" is a huge subject and this course covers only the basics of classification methodologies.
Please note that we have no capacity to analyse private data sets.
Prerequisites
- Good working knowledge of the Python programming language (version 3).
- Familiarity with NumPy, Pandas and MatPlotLib.
Practical information
Number of participants: minimum 5, maximum 12.
Length: The course takes one half-day, from 09:00 to 13:00 with 2 breaks.