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NTNU:TIA:REST

Institutt for industriell økonomi og teknologiledelse

Department of Industrial Economics and Technology Management

Engelsk

Anbefalte forkunnskaper

This course is designed for PhD candidates within the fields operations research, finance and economics. Programming skills are recommended.

Forkunnskapskrav

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

Link til informasjon om emnet

Faglig innhold

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). <a href="https://www.ntnu.edu/compama/">COMPAMA</a> 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.

Kursmateriell

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

Læringsutbytte

After having completed the course the candidate should be able to:<ul><li>explain and implement the different methods learned;</li><li>choose the more suitable method for a specific economic application;</li><li>recognize the opportunities and challenges of using AI in each context.</li></ul>

Læringsformer og aktiviteter

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.

Deltagelse og obligatoriske aktiviteter

Introduksjon til metoder for maskinlæring og kunstig intelligens med økonomiske anvendelser

Bedriftsøkonomi og optimering

Bedriftsøkonomi

Økonomi og ledelse - doktorgrad (ph.d.)

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This was the first time the course was organized.

This course is part of a portfolio of four Ph.D. courses developed under a research project funded by The Research Council of Norway, COMPAMA: https://www.ntnu.edu/compama/courses. These courses are designed to be given by an external lecturer (preferably an expert in the topic) in one teaching week. They are open to NTNU students and students from other universities in Norway and abroad. In this way, the assessment of the course is defined by the responsible for the course at NTNU and the external lecturer. In this specific course, we have defined to have a project to assess the knowledge assimilated by the students. The students were randomly divided into groups of 3 or 4 students, aiming that students from different areas learn from each other. During the teaching week, the students decided on the topic they wanted to work on. Then, they had about one month and a half to hand in the project. Essentially, the students were asked to choose the most suitable machine learning technique to solve their problem, explaining the reasons for that. They also needed to implement this model to their own data and interpret the results. Finally, they were encouraged to discuss the limitations of the chosen approach, particularly in comparison with a benchmark model. All these requests are in line with the learning outcomes of the course. Given the format of the course, we did not have a reference group. Instead, at the end of the course, an anonymous survey was shared with the students. Unfortunately, only two students have answered it, in a total of 10 students. Both students considered this type of evaluation appropriate for the course. However, while one student would have preferred to work with people with a similar research interest, the other said that it was a good learning experience to work with people with different backgrounds. This is a point that I will discuss internally to find the best solution for the next edition. In general, the students were satisfied with the lectures, even though they were online and sometimes quite heavy in content. They refer to the very good quality of the material made available to them (slides produced by the invited lecturer). Note that we also booked a room at NTNU for the students who prefer to be together in a room. The students informally said that they appreciate it. One student also said that it would be efficient to have more information about the course's organization and evaluation more time in advance, in order to organize schedules. We have posted some information before the course startup. But the working groups were only defined on the first teaching day. This will be taken into account in the next edition. In my opinion, the way the course is designed is relevant for the students and gives to them a good overview of the topic. Hopefully, the next edition will be on campus, which will make the relationship between the students and lecturer-students easier even more profitable.

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.

After having completed the course the candidate should be able to:<ul><li>explain and implement the different methods learned;</li><li>choose the more suitable method for a specific economic application;</li><li>recognize the opportunities and challenges of using AI in each context.</li></ul>

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Introduksjon til metoder for maskinlæring og kunstig intelligens med økonomiske anvendelser - IØ8812