Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After passing the course the students can conduct simple multivariate statistical analyses. They are familiar with common multivariate data analysis techniques. Students are familiar with different multivariate location and scatter functionals and the corresponding estimates and they understand the basic properties of these functionals. Students know how to apply principal component analysis and how to robustify the method. Students can conduct bivariate and multiple correspondence analysis and interpret the findings. Students are familiar with canonical correlation analysis and they can apply the method in practice. Students know several approaches to discriminant analysis and classification including different depth based methods. They are also able to assess the goodness of the classification. Students are familiar with hierarchical clustering methods and moving centers -type clustering methods and they understand the restrictions of these approaches. Moreover, students are not only able to apply multivariate methods in practice, but they also understand the mathematics and the reasoning behind the methods.
Schedule: 11.01.2021 - 15.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Pauliina Ilmonen
Teacher in charge (applies in this implementation): Pauliina Ilmonen
Contact information for the course (valid 07.12.2020-21.12.2112):
Zoom lectures: Pauliina Ilmonen, pauliina.ilmonen(a)aalto.fi
Zoom exercises: Nourhan Shafik, nourhan.shafik(a)aalto.fi and Anton Vavilov, anton.vavilov(a)aalto.fi
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The course is an introduction to multivariate statistical analysis. Course topics include multivariate location and scatter, principal component analysis (PCA), robustness and robust PCA, bivariate correspondence analysis, multiple correspondence analysis (MCA), canonical correlation analysis, discriminant analysis, statistical depth functions, classification and clustering. Software R is used in the exercises of this course.
Assessment Methods and Criteria
Homework assignments, exercise points, exam, compulsory project work.
Applies in this implementation:
Attend the zoom lectures and be active - not compulsory, no points, but highly recommended. Please note that the zoom lectures are not recorded, but the lecture slides are given in MyCourses under Materials.
Submit your project work on time - THIS IS COMPULSORY - max 6 points. (The deadline is 12.4. at 12.00 and late submission is not possible.)
Take the exam - max 24 points. (The course examinations is on Thursday 15.4.)
Participate to weekly zoom exercises (group 1, group 2, group 3 OR group 4) - not compulsory, but highly recommended - max 3 points. Please note that the zoom exercises are not recorded,
Be ready to present your homework solutions in the zoom exercise group - not compulsory, but highly recommended - max 3 points. Please note that the zoom exercises are not recorded,
Max total points = 6 + 24 + 3 + 3 = 36. You need at least 16 points in order to pass the course.
Lectures 24h (2), Exercises 24h (2), Project work 40h, Homework assignments 30 h, reading and studying the lecture materials 20 h
Lecture slides and lecture notes. Students are expected to either attend the lectures or ask for lecture notes from their fellow students.
Substitutes for Courses
Mat-2.3112 Statistical Multivariate Methods P
At least one statistics/probability course (preferably MS-C1620 Statistical Inference or equivalent) and one matrix algebra course.
- Teacher: Sami Helander
- Teacher: Pauliina Ilmonen
- Teacher: David Radnell
- Teacher: Paavo Raittinen
- Teacher: Nourhan Shafik
- Teacher: Anton Vavilov