Sunith Bandaru
Associate Professor of Production Engineering
School of Engineering Science
Computational Intelligence A1N
Course, Master's (2nd cycle),
6 credits,
VP702A
Application
Choose a course instance to see course syllabus and admission requirements.
Autumn 2024, Location: Skövde, Pace of study: 40%
Application
Application is done after nomination.
Course syllabus, with reading list
When? Where? How?
Study period: 2 September 2024 - 3 November 2024
Location: Skövde, Campus, Daytime
Pace of study: 40%
Admission Requirements
A Bachelor’s degree equivalent to a Swedish kandidatexamen of 180 credits, within the main fields of integrated product development, production engineering, automation engineering, mechanical engineering or information technology (or the equivalent).
A further requirement is proof of skills in English equivalent of studies at upper secondary level in Sweden, known as the Swedish course English 6. This is normally demonstrated by means of an internationally recognized language test, e.g. IELTS or TOEFL.
Selection
Guaranteed admission.
Language
The courses are conducted in English.
Computational Intelligence (CI) deals with methods and algorithms intended to solve NP-hard problems. Many problems in the real-world are ill-posed and therefore cannot be efficiently solved using deterministic methods. CI methodologies can be broadly classified into neuro, fuzzy and evolutionary techniques. These techniques emulate natural and biological systems to find approximate solutions in a reasonable time. CI also has a significant overlap with the fields of artificial intelligence and data-mining, where the focus is on discovering patterns and knowledge from data through machine learning.
This course will firstly introduce methodologies which fit the traditional definition of CI, namely: Artificial Neural Networks, Fuzzy Computation and Evolutionary Computation.
We will also learn to apply selected topics in machine learning, namely: supervised learning which includes Classification and regression trees, k-Nearest Neighbors, Neural Networks and Random Forests and Unsupervised learning which includes k-Means and Hierarchical Clustering.
Desirable Prerequisites are basic knowledge of vector and matrix operations and programming knowledge.