A series of seminars on the topic “Machine learning”

We invite you to a series of educational seminars focused on the machine learning methods led by Mgr. Zuzana Rošťáková, Ph.D. The first seminar lecture will take place on September 23, 2021 (Thursday) at 10:00 am in the meeting room of IMS SAS.

After the introduction to machine learning, we will focus on selected approaches to the graphical representation of multidimensional data and the dimensionality reduction methods.

 

Preliminary syllabus:

Introduction to machine learning

  • unsupervised learning
  • supervised learning
  • selected methods for graphical representation of multidimensional data in MATLAB

Unsupervised learning

a) Dimensionality reduction

  • principal component analysis and its version for functional data and time series
  • exploratory factor analysis
  • independent component analysis
  • non-negative matrix factorisation
  • higher-order arrays decomposition methods

b) Cluster analysis:

  • non-hierarchical clustering
  • hierarchical clustering

 

Supervised learning

a) Data classification

  • introduction to data classification
  • classifier’s quality
  • classification methods:
    • k-nearest neighbours
    • decision trees
    • linear and quadratic discriminant analysis
    • naive Bayesian classifier
    • support vector machines
    • neural networks

 

At the seminars, we will gradually take over the individual methods, their implementation in MATLAB, and application to real data.

 

Literature:

  1. electronic sources from lectures on Multivariate statistics from doc. Mgr. Radoslav Harman, PhD., (see source)
  1. James G., Witten D., Hastie T., Tibshirani R. (2021) An Introduction to Statistical Learning: with applications in R. Springer. Second Edition. ISBN: 978-1-0716-1417-4. https://doi.org/10.1007/978-1-0716-1418-1, (see source)
  2. Everitt B. S.(2005) An R and S-plus Companion to Multivariate Analysis. Springer. ISBN 978-1-85233-882-4. https://doi.org/10.1007/b138954
  3. Mohammed, M., Khan, M. B., Bashier, E. B. M. (2016). Machine learning: algorithms and applications. Crc Press. ISBN: 978-1-31537-165-8 https://doi.org/10.1201/9781315371658
  4. Lamoš, F., Potocký, R. (1998) Pravdepodobnosť a matematická štatistika (štatistické analýzy). Univerzita Komenského, Bratislava. ISBN: 80-223-1262-2