IT3105 - Artificial Intelligence Programming


Examination arrangement

Examination arrangement: Project Work
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Project Work 100/100

Course content

The course gives students the opportunity to implement many classic AI algorithms and use them as modules in large AI systems to perform tasks such as speech and image processing, simulated soccer (in the well-known Robocup on-line competition), Texas Hold'Em poker playing, and robot navigation. Some of the important AI methods that can appear in various projects include the A* algorithm, means-ends analysis, decision-tree learning, genetic algorithms, neural networks, bayesian classification, case-based reasoning, boosting and bagging. Through this work, students will gain an in-depth understanding of "AI in practice" as opposed to the combination of "AI in theory" and "AI on toy problems" that one experiences in the introductory and intermediate AI courses. The course will consist of 2-4 projects, depending up the year and the extent of the individual projects. Each project will be supported by a series of lectures on relevant theoretical and practical issues surrounding the problem domain, while some class meetings will be reserved for interactive discussions of the problem and student progress. Students will be free to program in the language of their choice, although Python, Java and C++ will be recommended.

Learning outcome

Students will gain hands-on experience designing and implementing relatively large AI projects. Students will gain valuable insights into why, when and how to use AI methods in realistic problems that they may encounter in their technical careers.

Learning methods and activities

50% standard lectures, and 50% interactive project discussions between students and teacher. Students will be allowed to work alone or in groups of 2 (but no larger)

Further on evaluation

Course evaluation consists of 1-3 projects, which, together, consist of 6 components. To pass the course, a student must receive a passing mark on at least 5 of the 6 components.

To retake the course, all 6 components must be new submissions

Required previous knowledge

This course is only available for students admitted to the specialization in Artificial Intelligence in Computer Science (MTDT, MIDT), Informatics (MIT/MSIT) and Industrial Economics and Technology Management (MTIØT).

Course materials

Lecture notes and complete project descriptions will be provided, as will any research articles of relevance to a project.For robotics projects, students will have access to a robot simulator and possibly to real robots (for a limited time).All materials are free.

Credit reductions

Course code Reduction From To
IT2105 7.5 AUTUMN 2008
MNFIT215 7.5 AUTUMN 2008
MNFIT215 7.5 AUTUMN 2008
More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Informatics
Contact information
Course coordinator:

Department with academic responsibility
Department of Computer Science


Examination arrangement: Project Work

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD Project Work 100/100



Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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