Machine Learning Coffee Seminar: "Unsupervised machine learning for matrix decompositions"

Lecturer : 
Erkki Oja
Event type: 
Event time: 
2017-01-09 09:15 to 10:00
Seminar room T6, CS building, Konemiehentie 2, Otaniemi
Starting January 9, Helsinki region machine learning researchers will start our week by an exciting machine learning talk and discussion over coffee. The talks will start 9:15, with coffee served from 9:00. The first talk is:
Unsupervised Machine Learning for Matrix Decomposition
Erkki Oja
Professor Emeritus, Aalto University
Unsupervised learning is a classical approach in pattern recognition and data analysis. Its importance is growing today, due to the increasing data volumes and the difficulty of obtaining statistically sufficient amounts of labelled training data. Typical analysis techniques using unsupervised learning are principal component analysis, independent component analysis, and cluster analysis. They can all be presented as decompositions of the data matrix containing the unlabeled  samples. Starting from the classical results, the author reviews some advances in the field up to the present day.
The next talks are:
16.1. at 9:15 in Kumpula Exactum D123: Arto Klami "Probabilistic programming: Bayesian modeling made easy"
23.1. at 9:15 in Otaniemi CS building T5: Juho Rousu "Metabolite identification through machine learning"
30.1. at 9:15 in Kumpula Exactum D123: Jukka Corander "Likelihood-free inference and predictions for computational epidemiology”

Last updated on 4 Jan 2017 by Noora Suominen de Rios - Page created on 3 Jan 2017 by Noora Suominen de Rios