GRA 4150 Artificial Intelligence - Technologies and Applications

GRA 4150 Artificial Intelligence - Technologies and Applications

Course code: 
GRA 4150
Department: 
Strategy and Entrepreneurship
Credits: 
6
Program of study: 
Master of Science in Business Analytics
Course coordinator: 
Ingunn Myrtveit
Erik Stensrud
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2020 Spring
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

There is currently a lot of hype and fear around AI, for example claims that super-intelligent machines will conquer the world, and we will all lose our jobs. The main course objectives are to demystify AI, learn how it is put into practice in businesses, and learn to assess its potential benefits as well as understand its limitations. 

This course gives an introduction to some key AI technologies and applications:

  • Algorithms: supervised machine learning such as artificial neural nets, search methods
  • Knowledge representation for the semantic web: knowledge graphs and ontologies.
  • AI applications: computer vision and robotics.

The course will also provide many examples of AI uses and the reality of AI technologies today, and an outlook on the future and impact on jobs.

Learning outcomes - Knowledge

After finishing the course the candidate should: 

  • know state-of-the-practice AI applications in various industries
  • understand the limitations and dangers of AI
  • understand basic concepts of AI technologies
  • know state-of-the-art and future trends in AI
  • understand algorithms written in pseudo-code
Learning outcomes - Skills

After finishing the course the candidate should: 

  • be able to program basic AI algorithms in Python
  • be able to transform pseudo-code into Python
  • be able to design a simple schema in RDF (Resource Description Framework) using OWL (Ontology Web Language) and query it using SPARQL (SPARQL Protocol and RDF Query Language)

 

General Competence

After finishing the course the candidate should: 

  • feel more confident when collaborating with AI specialists
  • be able to assess how the use of AI could give a competitive advantage
  • be able to read and understand AI literature on your own
Course content

Topics:

  • Overview of AI: definitions, history, hype, and predictions about the future
  • AI applications: computer vision, autonomous vehicles, and more
  • Machine Learning-based methods: artificial neural networks
  • Search-based methods
  • Knowledge-based methods and the semantic web
  • The future of AI
Teaching and learning activities

As this course is an elective, the teaching and learning activities are tentative and subject to adjustments based on class size.

The course will be a combination of lectures, tutorials, and programming exercises. The purpose of the latter is to enhance the understanding of AI methods. There will be guest lecturers from industry.

Software tools
No specified computer-based tools are required.
Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Required prerequisite knowledge
  • Linear regression analysis, at the level of GRA6020 Applied Data Analytics
  • Python, at the level of GRA4142 Data Management and Python Programming
  • Data modeling, at the level of GRA4142 Data Management and Python Programming
  • XML and JSON, at the level of GRA4142 Data Management and Python Programming
Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41501
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
30Yes1 Hour(s)
  • Bilingual dictionary
Individual Mid-term exam.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41502
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
70Yes3 Hour(s)
  • Bilingual dictionary
Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight: 30
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • Bilingual dictionary
Duration:1 Hour(s)
Comment:Mid-term exam.
Exam code:GRA 41501
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight: 70
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:
Exam code: GRA 41502
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Sum workload: 
0

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.