Statistical Classification in Machine Learning
Course duration: 12 h
Modern life requires automatic processing of huge amounts of data, impossible without assistance of powerful computers running state-of-the-art algorithms, in particular classification algorithms. Such fields of applied computer science as computer vision, speech recognition, and natural language processing are strongly dependent on the classification techniques, while speaker recognition, spam filtering, and biometric identification are pure classification tasks by the nature.
The course is meant to give an overview of statistical classification methods within the framework of machine learning. We will learn how to preprocess data to make it feasible for classification – the procedure known as feature extraction. Starting from simple linear models for single instance classification, we will proceed with neural networks and kernel methods. After this, sequence classification with hidden Markov models will be addressed.
Within the practical section we will apply learnt methods to real-life tasks using the state-of-the-art software.
Lectures
- Introduction into Machine Learning, Feature Extraction
- Linear models for Classification
- Neural Networks
- Kernel methods
- Graphical probabilistic models: Bayesian nets, Markov fields, HMMs
Tutor
Mr. Dmytro Prylipko
Country: Germany
Place of employment: Otto-von-Guericke University Magdeburg, Germany
Spheres of research: affect recognition from speech, modeling non-linguistics vocalizations in spontaneous speech, recognition of spontaneous speech, spoken dialog systems design
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Phone: +49 176 325 44 562