AI Applications in Marketing (MSB209)
This course aims to introduce students to novel quantitative approaches (such as artificial intelligence (AI) methods and text analytics) that can be applied to solve marketing problems and manage customer experience.
NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th for the autumn semester.
Course description for study year 2024-2025. Please note that changes may occur.
Course code
MSB209
Version
1
Credits (ECTS)
10
Semester tution start
Spring
Number of semesters
1
Exam semester
Spring
Language of instruction
English
Content
Learning outcome
Knowledge
Upon completion of the course, students will have knowledge of:
- Foundations of AI (deep learning, neural networks, etc)
- Data-driven approaches (such as AI methods, text analytics) applied to marketing data.
- Implications of new technologies such as AI on customer experience and related ethical considerations (eg customer privacy implications, etc)
Skills
- Analyze marketing data and apply AI methods and text analytics to support marketing decisions.
- Discuss the implications of new technologies such as AI on customer experience and related ethical considerations
Required prerequisite knowledge
Exam
School exam (individual) and Portfolio (group)
Form of assessment | Weight | Duration | Marks | Aid |
---|---|---|---|---|
School exam | 1/2 | 3 Hours | Letter grades | |
Portfolio (group) | 1/2 | 7 Days | Letter grades |
The final grade is based on an individual exam and a portfolio of mandatory work components, including group assignments. Students failing the portfolio evaluation will be granted the opportunity of taking a deferred exam. This exam will take the form of new written individual assignments.
Coursework requirements
The following are mandatory course requirements:
- Class participation (70%)
- Lab exercises
- Presentations
In order to take the exams, students must pass all coursework requirements.
70% attendance at all mandatory sessions starting from week 1 of teaching.
Course teacher(s)
Course coordinator:
Mainak SarkarCourse teacher:
Mainak SarkarMethod of work
Lectures and tutorials, labs, group work, and independent study. The estimated distribution of ECTS hours are as follows:
1. Lectures and tutorials: 50 hours
2. Group work and labs: 110 hours
3. Independent study of course material 120 hours