STE6246-002 Artificial Intelligence and Intelligent Agents - 5 ECTS
- 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
After passing this course the student should have obtained the following learning outcome:
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
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
- 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
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.
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.