Computational Intelligence A1N

Education facts

Code: VP702A

<p>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.</p> <p>This course will firstly introduce methodologies which fit the traditional definition of CI, namely: Artificial Neural Networks, Fuzzy Computation and Evolutionary Computation.</p> <p>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.</p> <p>Desirable Prerequisites are basic knowledge of vector and matrix operations and programming knowledge.</p>

Entry 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 or English level 2 (or the equivalent). This is normally demonstrated by means of an internationally recognized language test, e.g. IELTS or TOEFL.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Computational Intelligence A1N

  University of Skövde

  Skövde

  Start date: 31 August 2026

  End date: 1 November 2026

  Pace of study: 40 %