Course description forDAVE3625 Introduction to Artificial Intelligence
This course provides a broad introduction to Artificial Intelligence (AI), with methodologies and techniques that can be applied to different application domains. The course will balance theoretical approaches and practical tasks. Two broad AI areas will be covered, namely supervised and unsupervised methods. Among those, standard methods for regression, classification and clustering will be covered, e.g. support vector machines, nearest neighbor, decision tree, K-means, agglomerative and hierarchical clustering. An introduction to the usage of artificial neural networks and backpropagation algorithm will be provided.
Recommended preliminary courses
Basic programming skills (C, Python, Java, or similar programming language)
Discrete mathematics course at undergraduate level
Required preliminary courses
On successful completion of the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence.
Knows how the field of artificial intelligence developed historically
Is familiar with the main artificial intelligence theories and has a practical understanding of the development and use of artificial intelligence
Can reflect on the practical, social and ethical implications of the development of artificial intelligence
Has an understanding of the current application areas of artificial intelligence
Has the theoretical and practical skills to build simple artificial intelligence systems
Can use a variety of state-of-the-art artificial intelligence techniques in different application domains
Can evaluate the technical quality and practical value of various types of artificial intelligence
Has both theoretical and practical understanding of artificial intelligence methods
Can discuss the relevance, strengths and limitations of artificial intelligence methods
Is able to solve real-life problems using artificial intelligence methods
Teaching and learning methods
The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out in groups of 2 to 4 students. The project will be chosen from a portfolio of available problems, either from industry partners or by the research groups. The students will work in groups and will submit an academic report and give an oral presentation. Lab sessions supporting the assignments will be provided.
3 compulsory assignments done in groups of 2-4 students must be approved in order to be admitted to the final exam.
30% of the grade based on the academic report (in groups of 2-4 students, 5-10 pages written report with link to code e.g. on github).
70% of the grade based on individual written examination (3 hours).
Both exams must be passed in order to pass the course.
The exam result can be appealed.
Permitted Exam Materials and Equipment
No support materials are allowed for the written exam.
A grading scale of A (highest) to F (lowest) where A to E is a pass grade and F is a fail grade.
Two examiners. External examiner is used periodically.
- Course name in Norwegian
- Introduksjon til Kunstig Intelligens
- Study programme
- Fall: Bachelorstudium i ingeniørfag - data / Bachelorstudium i informasjonsteknologi / Bachelorstudium i anvendt datateknologi / Bachelorstudium i ingeniørfag - elektronikk og informasjonsteknologi
- 10 ECTS
- Year of study
- FALL 2019
- Programme description
- Fall 2019: Bachelorstudium i ingeniørfag - data / Bachelorstudium i informasjonsteknologi / Bachelorstudium i anvendt datateknologi / Bachelorstudium i ingeniørfag - elektronikk og informasjonsteknologi
- Subject History