GRA 4150 Artificial Intelligence - Technologies and Applications

GRA 4150 Artificial Intelligence - Technologies and Applications

Course code: 
GRA 4150
Department: 
Economics
Credits: 
6
Course coordinator: 
Leif Anders Thorsrud
Course name in Norwegian: 
Artificial Intelligence - Technologies and Applications
Product category: 
Master
Portfolio: 
MSc in Business Analytics
Semester: 
2021 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

The main course objectives are to demystify AI, learn AI methods, learn how AI is put into practice in businesses, and learn to assess the potential benefits of AI. 

Learning outcomes - Knowledge

After finishing the course the candidate should know: 

  • central AI methods
  • how AI applications are used in various industries
  • ethical and practical consideration for the use of AI
Learning outcomes - Skills

After finishing the course the candidate should be able to: 

  • program basic AI algorithms in Python
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:

  • A brief history of AI
  • Defining and measuring AI
  • AI versus Machine Learning
  • The role of Big Data in AI
  • Ethics in AI

The course will also provide many examples of AI uses and the reality of AI technologies today, including:

  • Computer vision
  • Natural language processing
  • One or two other topics TBD.

We will also discuss practical considerations for doing an AI project.

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:

  • Recorded lectures
  • External videos
  • Blogs/tutorials on specific topics
  • Guest lectures

There will be exercises to illustrate some of the topics in the course. Familiarity with python is expected, and some familiarity using machine learning packages (scikit-learn, XGBoost, pytorch, tensorflow) is a big advantage.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

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.

Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

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
Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 41503
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
100Yes3 Hour(s)
  • Bilingual dictionary
Individual .
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • Bilingual dictionary
Duration:3 Hour(s)
Comment:.
Exam code: GRA 41503
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36
The course will be a combination of lectures, tutorials, and programming exercises. There will be guest lecturers from industry.
Student's own work with learning resources
61 Hour(s)
Group work / Assignments
60 Hour(s)
Examination
3 Hour(s)
Sum workload: 
160

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.