Lecturers: Pekka Malo & Eeva Vilkkumaa
Course assistants: Anton Frantsev & Lauri Neuvonen
The course focuses on bridging the gap between predictive and prescriptive modelling. This will entail combining probabilistic modelling approaches with modern optimization techniques and decision-analytic tools. In terms of content, the course consists of two parts. In the first part, the students will learn methods and theory (e.g., optimization concepts and fundamentals of statistical learning theory) needed for prescriptive modelling. The material will involve programming assignments with practical applications. The second part will feature applications in the form of visiting lectures, who come different industries (e.g., financial analytics, sports analytics).
After completing the course, students will
- understand the importance of prescriptive analytics in business decision-making
- be able to combine predictive modeling approaches with optimization techniques to build prescriptive analytics solutions, and
- be able to implement (program) their solutions with suitable software.
Assessment and grading
Course assessment is comprised of the following two parts:
- Team case (course project): 70%
- Class activity (tutorials, lectures, exercises): 30%.
All assignments must be completed to pass the course. Late assignments will not be accepted. All the assignments are assessed on a 0-5 scale based on the rubrics that are available in the course workspace in Aalto MyCourses. Note that the starting level of the student teams will be taken into account in grading, and thus special attention is paid to the teams’ development in knowledge sharing and learning.
Additional information can be found in the course syllabus.