Course - Advanced course in economic applications of machine learning and AI - IØ8813
IØ8813 - Advanced course in economic applications of machine learning and AI
About
Examination arrangement
Examination arrangement: Assignments
Grade: Passed / Not Passed
| 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 within 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 algorithmic trading, portfolio optimization and dynamic pricing. The main topic will be reinforcement learning.
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
Recommended previous knowledge
This course is designed for PhD candidates within the fields operations research, finance and economics. Knowledge of machine learning methods is recommended. Such knowledge can be obtained through the course Introduction to machine learning and AI methods with economic applications. Programming skills are also needed.
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.
Version: 1
Credits:
2.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: SPRING 2024
Language of instruction: English
Location: Trondheim
- Managerial Economics, Finance and Operations Research
- Business Economics
Department with academic responsibility
Department of Industrial Economics and Technology Management
Examination
Examination arrangement: Assignments
- Term Status code Evaluation Weighting Examination aids Date Time Examination system 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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"