SPIS2001 Machine Learning and Artificial Intelligence in Games
- Course codeSPIS2001
- Number of credits10
- Teaching semester2026 Spring
- Language of instructionEnglish
- CampusHamar
- Required prerequisite knowledge
Recommended: 2VSIM101 Visualisation and simulation
- Artificial Neural Network
- Q-learning Reinforcement Learning
- Genetic Algorithm Learning
- Waypoint Navigations
- Behaviour Tree
Learning Outcome
Once the student has passed the course, they will have achieved the following learning outcomes:
The student
- understands the complexity of various machine learning (ML) algorithms and their limitations in training
- understands issues and roles of Artificial Intelligence (AI) in the design of games
- understands tactical and strategic AI used in gaming scenario
The student
- can explain the general idea behind ML, as well as specific algorithms that are being used in real world scenarios
- can use ML methodology for research and industrial settings using current trend of ML software libraries
- can implement and apply ML methods to any chosen game
- can perform experiments in ML using real-world game scenario
- is capable of confidently applying common ML algorithms in practice and implementing their own algorithm
- can program autonomous movement of avatars
- can read and understand scientific publications on ML/AI and formulate current issues, choice of methods, and results in a short, concise manner
- capable of producing games where the avatar navigates around and performs actions based on waypoint, behaviour tree
- can design game to have the avatar to learn about the playing scenario based on genetic algorithm and artificial neural network
The student
- is able to plan and carry out varied tasks in accordance with ethical requirements and guidelines
- is familiar with relevant issues of professional ethics, and is able to make a contribution to a professional community
The course is organized as a combination of lectures, practical exercises and supervision.
- 3–5 individual assignments for machine learning and artificial intelligence respectively
Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
---|---|---|---|---|---|---|
Portfolio Assessment | ECTS - A-F | Individual |
| 50 | Folder 1 - Artificial intelligence | |
Portfolio Assessment | ECTS - A-F | Individual |
| 50 | Folder 2 - Machine learning |
- 1 individual portfolio assessment on artificial intelligence which counts for 50% the final grade
- 1 individual portfolio assessment on machine learning which counts for 50% the final grade
Reading list
No reading list available for this course