Learning with MOOCs: promises and problems

Massive Open Online Courses (MOOCs) may be considered to be a new form of virtual technology enhanced learning environments. Since their first appearance in 2008, the increase in the number of MOOCs has been dramatic. The hype about MOOCs was accompanied by great expectations: 2012 was named the Year of the MOOCs and it was expected that MOOCs would revolutionise higher education. Two types of MOOCs may be distinguished: cMOOCs as proposed by Siemens, based on his ideas of connectivism, and xMOOCs developed in institutions such as Stanford and MIT. Although MOOCs have received a great deal of attention, they have also met with criticism. The time has therefore come to critically reflect upon this phenomenon.

Combined Machine Learning Techniques for Decision Making Support in Medicine

Computational intelligent support for decision making is becoming increasingly popular and essential among medical professionals. Also, with the modern medical devices being capable to communicate with ICT, created models can easily find practical translation into software. Machine learning solutions for medicine range from the robust but opaque paradigms of support vector machines and neural networks to the also performant, yet more comprehensible, decision trees and rule-based models. So how can such different techniques be combined such that the professional obtains the whole spectrum of their particular advantages? The presented approaches have been conceived for various medical problems, while permanently bearing in mind the balance between good accuracy and understandable interpretation of the decision in order to truly establish a trustworthy ‘artificial’ second opinion for the medical expert.

Combination of Evolutionary Algorithms with Experimental Design, Traditional Optimization and Machine Learning

Evolutionary algorithms alone cannot solve optimization problems very efficiently since there are many random (not very rational) decisions in these algorithms. Combination of evolutionary algorithms and other techniques have been proven to be an efficient optimization methodology. In this talk, I will explain the basic ideas of our three algorithms along this line (1): Orthogonal genetic algorithm which treats crossover/mutation as an experimental design problem, (2) Multiobjective evolutionary algorithm based on decomposition (MOEA/D) which uses decomposition techniques from traditional mathematical programming in multiobjective optimization evolutionary algorithm, and (3) Regular model based multiobjective estimation of distribution algorithms (RM-MEDA) which uses the regular property and machine learning methods for improving multiobjective evolutionary algorithms.