course-details-portlet

IØ8813 - Advanced course in economic applications of machine learning and AI

About

New from the academic year 2021/2022

Examination arrangement

Examination arrangement: Assignments
Grade: Passed/Failed

Evaluation Weighting Duration Grade deviation Examination aids
Assignments 100/100

Course content

Advanced course in economic applications of machine learning and AI 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 extend the knowledge in machine learning methods applied to economics, going beyond the traditional unsupervised and supervised methods. The main goal is that the students can understand and apply sophisticated models in economic applications. Examples of relevant applications are algorithm trading, portfolio optimization and dynamic pricing.

Learning outcome

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

  • explain and implement the techniques learned;
  • choose the more suitable approach 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

Specific conditions

Compulsory activities from previous semester may be approved by the department.

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

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  SPRING 2022

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

Examination

Examination arrangement: Assignments

Term Status code Evaluation Weighting Examination aids Date Time Digital exam Room *
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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