ACIT4620 Computational Intelligence: Theory and Applications Course description

This course will cover the fundamentals of computational intelligence (CI) techniques, modern approaches to artificial intelligence (AI), as well as several advanced topics such as neuro-fuzzy systems and neuro-evolution. The main topics include definitions of AI and CI, history of AI and CI, symbolic vs. connectionist AI methods, mainstream CI approaches (artificial neural networks, fuzzy systems, and evolutionary computation), and some representative applications of CI. The course will illustrate those CI approaches using various application examples from different fields (for example, engineering and biomedicine). In addition, new trends, opportunities and challenges in the CI field will be covered.

Basic knowledge in calculus, statistics and probability theory; Programming skills in Python, Matlab, or R.

Students are expected to have the following learning outcomes in terms of knowledge, skills and general competence.

Knowledge

On successful completion of the course, the students have:

• an in-depth understanding of state of the art Computational Intelligence (CI) methods (fuzzy sets and systems, artificial neural networks, evolutionary computation, and parts of machine learning).

• knowledge and understanding of open problems and future trends in the CI field.

Skills

On successful completion of the course, the students can: 

• apply appropriate CI models and algorithms to address modeling and optimization problems in real-world applications. 

• analyze complex and uncertain datasets with CI algorithms.

General competence

On successful completion of the course, the students can:

• implement CI algorithms by programming. 

• deploy CI systems/models in real-world applications. 

• solve complex optimization or decision-making problems using evolutionary algorithms.

The course consists of lectures, seminars and group discussions on methods and algorithms, as well as a project to be carried out in groups. The project will be chosen from a list of available research problems. The students will work in groups and will submit the code and a project report. 

Practical training

Lab sessions.

The following two mandatory assignments must be approved before the student can take the final exam:

• Individual: One individual oral presentation on a given topic.
• Group-based: A (final) group project proposal (maximum 1000 words), containing a brief description of the research topic, the available dataset(s), the method/algorithm to be employed, and some references (including several most recent journal papers).

Exam in two parts:

• A group (2-4 students) project implementation, consisting of a project report (7000 - 9000 words, excluding references) and code appendix (counts 50% of the final grade)
• A written exam (3 hours) (counts 50% of the final grade)

Both the code/program and the report will be evaluated. The comprehensiveness of the code/program is evaluated under the assumption that each student in the group has worked on the project for 60 hours. 

Both exams must be passed in order to pass the course.

The exam can be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids are permitted for the group project. For the written exam; Calculator handed out by the university

Grade scale A-F.

Two internal examiner. External examiner is used periodically.

Professor Jianhua Zhang