Course - Artificial Intelligence Programming - IT3105
IT3105 - Artificial Intelligence Programming
Examination arrangement: Work
Grade: Passed / Not Passed
|Evaluation||Weighting||Duration||Grade deviation||Examination aids|
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.
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)
- Work 1
- Work 2
- Work 3
- Work 4
- Work 5
- Work 6
Further on evaluation
Each of the 6 assignments are graded as pass/fail. To pass the course, a student must receive
a passing mark on at least 5 of the 6 assignments. In some cases, 2 or more assignments may be
combined into one "project", so a student may pass one part of the project while failing another part.
To retake the course, a student must deliver all 6 assignments at a later date.
Compulsory activities from previous semester may be approved by the department.
Admission to a programme of study is required:
Computer Science (MIDT)
Computer Science (MTDT)
Industrial Economics and Technology Management (MTIØT)
Informatics (MIT) - some programmes
Recommended previous knowledge
TDT4120 Algorithms and Data Structures, TDT4136 Introduction to Artificial Intelligence, TDT4171 Artificial Intelligence Methods, and requires previous knowledge in Discrete Mathematics comparable with MA0301 Elementary Discrete Mathematics.
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).
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.
Credits: 7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2023
Language of instruction: English
Examination arrangement: Work
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD 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"