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IØ8812 - Introduction to machine learning and AI methods with economic applications

About

Examination arrangement

Examination arrangement: Assignments
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Assignments 100/100

Course content

Introduction to machine learning and AI methods with economic applications is an intensive PhD course offered through the project "COMPutational economics and optimization - Agents, Machines and Artificial intelligence" (COMPAMA). COMPAMA is developing an emerging interdisciplinary area in the borderland between economics, optimization, psychology, machine learning and AI with the main purpose to understand the economic impact of decisions, made by both machines and human agents.

This course will give an overview of machine learning methods within the AI framework. Economic applications for the learned methods will be presented and explored. The main goal is that students without previous knowledge in the area of machine learning and AI can understand and apply the models in their research topic. Examples of relevant applications are customer and marketing segmentation, credit risk assessment, forecasting and fraud detection.

Learning outcome

After having completed the course the candidate should be able to:

  • explain and implement the different methods learned;
  • choose the more suitable method for a specific economic application;
  • recognize the opportunities and challenges of using AI in each context.

Learning methods and activities

Lectures. Participation in the seminars is expected, which includes attendance at all lectures, as well as contributions to the discussions. There will be compulsory activities in the course.

Compulsory assignments

  • Participation and compulsory activities

Required previous knowledge

Admission to a PhD programme within operations research, or completed masters courses in optimization.

Course materials

Selected literature. Will be given at course start-up.

More on the course

Facts

Version: 1
Credits:  2.5 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2023

Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Managerial Economics, Finance and Operations Research
  • Business Economics
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Industrial Economics and Technology Management

Timetable

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Examination

Examination arrangement: Assignments

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD Assignments 100/100
Room Building Number of candidates
Spring ORD Assignments 100/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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