Computational Intelligence and Decision Making– P170M109
MODULE TYPE: SDG11
This course provides a comprehensive introduction to computational intelligence through the machine learning and intelligent decision making systems. The course covers the basics: Linear Discriminants analysis, Support Vector Machines, Learning with Trees, Ensemble Learning, Probabilistic Modelling, Unsupervised and Semi-Supervised Learning, Online learning strategies, Optimization and Search, Evolutionary learning, Markov chain Monte Carlo method and others. The course will also draw from numerous case studies and applications, so that you'll also learn how to use and integrate machine learning methods into the decision making system. Practical tasks are provided in MATLAB environment.
Get knowledge about the most popular machine learning methods, know possibilities of applications and understand the principles of learning algorithms.
Get knowledge about decision making systems, basic components and principles of creating architecture.
Ability to implement machine learning methods in MATLAB environment. Make comparative result analysis of selected methods.
Ability to select and apply the most suitable machine learning method(s) depending on the given problem.
Ability to integrate machine learning methods into the decision making systems.
Module level: Advanced
Degree level: Master
Study mode: Online
SDG11 Theme: Resilient communities
Starting date: 1 February 2021
Finishing date: 31 May 2021
Enrollment period: TBA
Additional Info: Max 30 students