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  4. Course structure

Course structure: Kunstig intelligens, sivilingeniør - master

Utveksling kan gjennomføres i 5. semester. Emnet Bærekraftig innovasjon skal inn i 9. semester. 

Emnene som listes opp ved å trykke på lenkene Mulige valgemner i tabellen under er bare utvalgte eksempler, det vil blir flere valgemner tilgjengelig. Merk også at valgemnene ikke er kollisjonssikret med tanke på undervisning / eksamen - dette er noe studentene selv må sjekke.
1. year
1. semester:
Innføring i programmering (5 ects)
Datamaskiners virkemåte og programmering (5 ects)
Introduksjon til kunstig intelligens, KI (10 ects)
Kalkulus (10 ects)
1. year
2. semester:
Objektorientert programmering (10 ects)
Lineær algebra (10 ects)
Statistikk og sannsynlighet (10 ects)
2. year
3. semester:
Vektorkalkulus (10 ects)
Numerical Methods (10 ects)
Machine Learning (10 ects)
2. year
4. semester:
Datastrukturer og algoritmer (10 ects)
Artificial Intelligence, AI - Methods and applications (10 ects)
Examen philosophicum, Tromsøvarianten (10 ects)
3. year
5. semester:
Computer architecture and organization (10 ects)
Optional courses
3. year
6. semester:
Operating Systems Fundamentals (10 ects)
Image Analysis (10 ects)
Optional courses
4. year
7. semester:
Computer Systems Experiments, Methodologies, Performance Measurements and Evaluation (10 ects)
Distributed Systems Fundamentals (10 ects)
Pattern recognition (10 ects)
4. year
8. semester:
Advanced Distributed Systems (10 ects)
Deep learning (10 ects)
Optional courses
5. year
9. semester:
Parallel Programming (10 ects)
Project Thesis in Artificial Intelligence (10 ects)
5. year
10. semester:
Master's Thesis in Artificial Intelligence (30 ects)
Innføring i programmering (5 ects)
Datamaskiners virkemåte og programmering (5 ects)
Introduksjon til kunstig intelligens, KI (10 ects)
Kalkulus (10 ects)
Objektorientert programmering (10 ects)
Lineær algebra (10 ects)
Statistikk og sannsynlighet (10 ects)
Vektorkalkulus (10 ects)
Numerical Methods (10 ects)
Machine Learning (10 ects)
Datastrukturer og algoritmer (10 ects)
Artificial Intelligence, AI - Methods and applications (10 ects)
Examen philosophicum, Tromsøvarianten (10 ects)
Computer architecture and organization (10 ects)
Optional courses
Operating Systems Fundamentals (10 ects)
Image Analysis (10 ects)
Optional courses
Computer Systems Experiments, Methodologies, Performance Measurements and Evaluation (10 ects)
Distributed Systems Fundamentals (10 ects)
Pattern recognition (10 ects)
Advanced Distributed Systems (10 ects)
Deep learning (10 ects)
Optional courses
Parallel Programming (10 ects)
Project Thesis in Artificial Intelligence (10 ects)
Master's Thesis in Artificial Intelligence (30 ects)
1. semester
2. semester
3. semester
4. semester
5. semester
6. semester
7. semester
8. semester
9. semester
10. semester
1. year
1. semester:
Innføring i programmering (5 ects)
Datamaskiners virkemåte og programmering (5 ects)
Introduksjon til kunstig intelligens, KI (10 ects)
Kalkulus (10 ects)
1. year
2. semester:
Objektorientert programmering (10 ects)
Lineær algebra (10 ects)
Statistikk og sannsynlighet (10 ects)
2. year
3. semester:
Vektorkalkulus (10 ects)
Numerical Methods (10 ects)
Machine Learning (10 ects)
2. year
4. semester:
Datastrukturer og algoritmer (10 ects)
Artificial Intelligence, AI - Methods and applications (10 ects)
Examen philosophicum, Tromsøvarianten (10 ects)
3. year
5. semester:
Theoretical Statistics (10 ects)
Optional courses
3. year
6. semester:
Tidsrekker (10 ects)
Image Analysis (10 ects)
Optional courses
4. year
7. semester:
Machine Vision (10 ects)
Stochastic Processes (10 ects)
Pattern recognition (10 ects)
4. year
8. semester:
Computer-intensive Statistics (10 ects)
Deep learning (10 ects)
Optional courses
5. year
9. semester:
Health data analytics (10 ects)
Project Thesis in Artificial Intelligence (10 ects)
5. year
10. semester:
Master's Thesis in Artificial Intelligence (30 ects)
Innføring i programmering (5 ects)
Datamaskiners virkemåte og programmering (5 ects)
Introduksjon til kunstig intelligens, KI (10 ects)
Kalkulus (10 ects)
Objektorientert programmering (10 ects)
Lineær algebra (10 ects)
Statistikk og sannsynlighet (10 ects)
Vektorkalkulus (10 ects)
Numerical Methods (10 ects)
Machine Learning (10 ects)
Datastrukturer og algoritmer (10 ects)
Artificial Intelligence, AI - Methods and applications (10 ects)
Examen philosophicum, Tromsøvarianten (10 ects)
Theoretical Statistics (10 ects)
Optional courses
Tidsrekker (10 ects)
Image Analysis (10 ects)
Optional courses
Machine Vision (10 ects)
Stochastic Processes (10 ects)
Pattern recognition (10 ects)
Computer-intensive Statistics (10 ects)
Deep learning (10 ects)
Optional courses
Health data analytics (10 ects)
Project Thesis in Artificial Intelligence (10 ects)
Master's Thesis in Artificial Intelligence (30 ects)
1. semester
2. semester
3. semester
4. semester
5. semester
6. semester
7. semester
8. semester
9. semester
10. semester