Type of course

Admission requirements

A relevant undergraduate Bachelor degree in Engineering program in computer science or equivalent.

In addition, the following requirements must be met:

- minimum 25 credits in mathematics (equivalent to Mathematical Methods 1, 2 og 3), 5 credits in statistics and 7,5 ects i physics on a higher level is required.

Courses at the Master degree program in Computer Science.

Recommended Prerequisite: Basic course in mathematical statistics.  

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

15 hours introductory lectures.

Individual project run by the students under guidance by a supervisor.

Teaching and examination Language:

Lectures will be held in English (Provided the presence of English speaking students, English will be chosen).

Lecture notes will be given in English.

Project report must be written in the English language.

Information to incoming exchange students

This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements" section".

Master Level

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412.