ACIT4620 Computational Intelligence: Theory and Applications Emneplan

Computational Intelligence is concerned with modern, bio-inspired approaches to artificial intelligence (AI) and is an umbrella term for the fields of neural networks (NN), fuzzy systems (FS) and evolutionary computation (EC). This course offers a comprehensive and systematic introduction to the fundamental concepts, principles, and methods in the three fields, a part of machine learning and deep learning, and several advanced topics (neuro-fuzzy systems, neuro-evolution, or fuzzy clustering). The course will illustrate major CI concepts, principles and methods using various application examples in engineering, biomedicine and business. In addition, the overview, history, state-of-the-art, and future trends of AI and CI field will be covered. The main modules for lectures include:

• AI and CI: Overview and history
• Fundamentals of neural networks
• Introduction to deep learning
• Fuzzy sets, logic and systems
• Topics in evolutionary computation
• Advanced topics
• AI and CI: State-of-the-art and future

It is recommended that students have some background knowledge in:

1) mathematics: calculus, linear algebra, statistics and probability theory, and numeric optimization

2) programming language in Python, Matlab or R

3) machine learning and/or data mining.

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 overview on different perspectives, history and future of AI and Computational Intelligence (CI) fields.
• familiarity with the essential terminologies, concepts, ideas, elements and principles in the three pillar fields of CI.
• an in-depth understanding of state-of-the-art CI methods (fuzzy systems, neural networks, evolutionary computation, deep learning, and hybrid AI techniques).
• knowledge and understanding of open problems and future challenges and opportunities in the AI and CI field.

Skills

On successful completion of the course, the students can:

• determine when to use and deploy the CI methods learned for real-world applications.
• 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:

• program the CI models/algorithms.
• deploy CI systems/models in real-world applications.
• solve complex search, optimization or decision-making problems using evolutionary algorithms.

The course consists of lectures (theory), labs (practical exercises and computer simulations/experiments), group discussions, Q&As, as well as group projects. The group projects will be assigned from a list of the suggested topics/areas. The students will work in groups and finally submit the project report as well as the code.

Practical exercises: Lab and Q&A sessions.

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

• 1 - Group report and presentation: Group written report and oral presentation on the assigned topic.
• 2 - Group project proposal: A group project proposal (1000 - 1200 words) on the assigned topic, containing project description, the available dataset(s), method/algorithm to be employed, and references (including several most recent journal publications).

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of this course the student has:

• advanced knowledge of multimodal user interfaces
• advanced knowledge of input and output technologies

Skills

On successful completion of this course the student can:

• analyse problems and issues in interactions related to context, such as accessibility in public spaces, mobility problems, and the user's affective state
• use knowledge of interaction technology to address new problems in universal design of ICT
• independently use appropriate methods of user centred interaction design and evaluation; both heuristic and automatic, in an independent manner
• analyse and critically deal with the results from relevant research literature, apply these to structure and formulate scientific arguments, and assess the suitability of published results on new problems and issues
• carry out independent, limited research or development projects under supervision and in accordance with applicable ethical standards
• present scientific work orally
• debate and conduct scientific discussions

General competence

On successful completion of this course the student can:

• apply knowledge and skills in interaction technology on new problems and issues for carrying out advanced facilitation tasks and projects
• communicate scientific problems, analysis and conclusions in the field to both specialists and the general public
• contribute to original thinking and innovation processes

- This course is organized as a series of lectures and seminars where students present and discuss with opponents research articles that cover core concepts and topics in the literature.

- Students work alone or in groups on two projects under supervision.

The following coursework must be approved before the student can take the exam:

• Two oral presentations of research articles.
• Participation in the discussion of at least two research article presentations.

• One written group project report (2000-3000 words) completed in group of up to 2 students related to practical implementation of HCI. This part of the examination counts 35 % of the final grade.
• One written group project report (3000-5000 words) completed in groups of up to 4 students, focused on theoretical aspects of the selected project topic. This part of the examination counts 35 % of the final grade.
• Individual oral examination (20 minutes for each candidate). The oral examination counts 30% of the final grade.

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

The oral examination cannot 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 registering 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.

Professor Jianhua Zhang