Special Course in Unsupervised Machine Learning: Probabilistic Factor Analysis M

Algoritmit ja koneoppiminen
Syventävät opinnot
This course will discuss probabilistic factor analysis methods within the domain of unsupervised machine learning. Factor analysis approaches are characterized by their ability to learn representations that summarize the data and are, therefore, widely used in data analysis and research. The course will cover factorization methods for matrices and tensors (higher-order matrices of three or more modes), as well as factorizations for multiple joint matrices and tensors. The methods will be introduced with their theoretical and statistical basis, while emphasis will also be laid on their computational implementation and interpretability in practical applications.
Vuosi Lukukausi Päivämäärä Periodi Kieli Vastuuhenkilö
2017 kesä 15.05-19.05. 5-5 Englanti Reijo Sivén