Application deadline

Type of course

Admission requirements

A relevant undergraduate bachelor Engineering programme with minimum 30 credits mathematic/statistics topics

Application Code: 9371

Required prerequisite(s) knowledge:

• STE6246-001 Knowledge based systems, theoretical part
• SMN6139 Discrete mathematics

Course content

¿ The students will have an introduction in time series and regressions supporting predictions based on historic data.  ARMA techniques will be addressed.

¿ Preprocessing techniques like PCA will be practiced ¿ Practical use of cluster techniques, Support Vector Machines and Classification and Regression trees (CART) will be exercised

¿ The course will explore in depth a selection of neural networks such as LSTM and CNN.

¿ The students will be introduced to MAS systems and theoretical concepts associated with these i.e. ontologies, ACL, architectures, game theory and MAS-learning

Recommended prerequisites

Objectives of the course

After passing this course the student should have obtained the following learning outcome:

Knowledge The student shall have; 

• Deep insight in a selection of regression, clustering and neural network methods
• Deep insight in CART and ensemble methods
• Hands-on knowledge in using a data scientific method on a practical problem
• Knowledge of agent-based learning and multi-agent systems (MAS)
• Hands-on knowledge in using MAS-methods on a practical problem

Skills The student should be able to;

• To set up data scientific/machine learning projects
• To include and use advanced software libraries for machine learning and MAS in their own program
• To interpret machine learning results with respect to application and business impact
• To set up distributed control systems using MAS

General Competence

• The student will be posed for industrial artificial intelligence and machine learning projects
• Be capable of creating smaller distributed control and optimization systems with MAS
• Be able to enter industrial projects on Big Data

Language of instruction and examination

Teaching methods

Assessment

Course work requirement: Minimum requirements for reporting structure and written format prior to submission. Students have to use software at the UiT in Narvik.

Examination and assessment: Project report (100%). Letter grading A - F, where F is fail grade. There will not be arranged a re-sit exam for this course.  

Recommended reading/syllabus