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Type of course

Admission requirements

Admission requirements: Higher Education Entrance Requirement + Bachelor's degree in Computer Science or similar education. The Bachelor degree must contain a specialization in Computer Science worth the equivalent of not less than 80 ECTS credits.

Application code: 9371 - Singular courses at master's level

Course content

The seminar deals with state-of-the-art research topics in computer science not covered by regular courses. The seminar content is normally linked to ongoing research activities at the research groups. The seminar will normally have a different content each time it is given. A seminar specific content will be made and preapproved by the programme board every time it is given.

The content of this seminar will be:

• AI techniques for different application areas
• Presentation of chosen project and demonstration of prototype for AI project

The course gives an overview of Artificial Intelligence and Applied Methods. The focus is on several different areas of Artificial Intelligence with AI-problems, and Methods and includes areas such as: Intelligent /Knowledge-based systems, Agent / multi-agent systems, Machine Learning, Artificial Neural Networks, Natural language processing and strategies.

The course addresses classic principles for design and implementation of AI systems and applications and discusses emerging trends from the international research front.

Particularly, the following topics will be addressed:

• Fundamental AI problems and solutions including search algorithms and planning, knowledge representation forms and knowledge including reasoning strategies, decision support and heuristics.
• Decision-support systems
• Intelligent agents and multi-agent systems
• Machine learning and neural networks.
• Automatic analysis and generation of natural language.

The focus is on using artificial intelligence techniques to develop systems for different problems.  

Objectives of the course

A general educational aim of the seminar will be to:

• expose students to state-of-the-art research topics in computer science
• help the students to develop research and independent learning
• broaden students' understanding of computer science by introducing additional special topics into the curriculum.

A more detailed list of learning outcomes on successful completion of the seminar will be made and preapproved by the programme board every time it is given. Detailed learning outcomes for this seminar is:

Knowledge - the student has:

• Knowledge of state of the art literature on AI and its application fields
• Knowledge of design and implementation principles of modern AI systems and applications with AI methods and technologies.
• Hands on experience with design and implementation of AI systems and applications.
• Knowledge of how advanced AI systems and applications can be contributed to a chosen AI project.

Skills - the student has / can:

• Master discussion of AI systems and applications.
• Skills in prototyping of AI systems and applications including its architecture, design, and implementation.
• Analyze the behavior of the developed AI prototype(s).
• Skills in presenting AI system(s) and application(s).
• Identify research problems and challenges in AI.
• Manage to discuss how AI systems and applications can contribute to better AI solutions.
• Lists the state-of-art tools and techniques for addressing research problems and challenges in AI systems and applications.

General competence - the student has / can:

• Competence to read scientific literature related to AI and carefully extract information from it and present and discuss it coherently in public.
• Competence in selecting adequate approach to design and implementation of AI systems and applications.
• Conduct reviews, writing, and presentations of AI and mobile systems.
• Take part in discussions of AI systems and applications.

Language of instruction

Teaching methods

To be specified according to 10 ECTS and consist of various combinations of lectures, seminars, colloquium and laboratory work as well as a written report. This course will consist of: 

Lectures: 20 hours, Seminars and Colloquium: 30 hours, Laboratory guidance: 20 hours. 

If applicable, 3 hours seminar at a company.

Assessment

Assessment methods and course requirements will vary from seminar to seminar. For each seminar it will be chosen an assessment method and course requirements that are academically appropriate. Assessment methods and course requirements for this seminar will be:

The exam consists of the following parts:

• Main assignment counting 50%: A project work which consists two parts:
  • A main programming task, which must be presented orally. The main task consists of choosing an AI-technique and developing a software system that solves an AI-problem. The task is carried out in a group with two students. The programming task must be presented at a seminar.
  • A report describing the state-of the art for the chosen AI-technique, for the AI-program, and a well-founded description of the AI-program. The report must have the form of a conference paper with, at least, 6 pages two columns.
• Written exam 4 hours, counting 50%

To achieve a final grade in the course all exam parts must obtain a passed letter grade. The exam parts are assessed as a whole and one combined grade is given. Assessment scale: Letter grades A-E, F - fail. Approved assignments give access to the exam.

Course requirements: The coursework includes 7 written assignments graded "Approved" / "Not approved".

The assignments are:

• Fundamental AI Problems and Solutions + Search Algorithms, Scheduling and Planning
• Expert systems / Knowledge-based systems, Intelligent systems, Knowledge representation
• Reasoning Strategies, Decision Support and Heuristics
• Multi-Agent Systems, Intelligent Agents and Meta-Agents
• Machine learning
• Neural networks
• Natural language: parsing and generation

Re-sit examination: It will not be given a re-sit examination in in this course.

Postponed examination: Students with valid grounds for absence will be offered a postponed examination for the module in question. If practically possible the examination is arranged during the semester as soon as the reasons for absence have ceased.

Date for examination

The date for the exam can be changed. The final date will be announced in the StudentWeb early in May and early in November.

Recommended reading/syllabus