Having knowledge about various multivariate statistical methods. Learning the analysis, classification and clustering methods.
Prerequisite(s)
-
Corequisite(s)
-
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Assist. Prof. Dr. Hikmet Çağlar, Assist. Prof. Dr. Canan Akkoyunlu
Anderson, T.W., An Introduction to Multivariate Statistical Analysis, 3rd ed. Wiley, (2003).
Other Sources
• Hastie, T., Tibshirani, R. and Freedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Perdition. Springer.
• Hardle, W. and Simar, L. (2003). Applied Multivariate Statistical Analysis. Springer.
• Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979). Multivariate Analysis. Academic Press.
Course Schedules
Week
Contents
Learning Methods
1. Week
Multivariable normal change
Lecture and recitation
2. Week
Traditional derivation: multivariable regression
Lecture and recitation
3. Week
Links with mixed linear models and hierarchical modeling
Lecture and recitation
4. Week
Techniques based on eigenvalue and singular decomposition.
Lecture and recitation
5. Week
SVD of a data matrix; specific decomposition
Lecture and recitation
6. Week
Main component analysis
Lecture and recitation
7. Week
Factor analysis, Canonical Correlation
Lecture and recitation
8. Week
Midterm
Exam
9. Week
Classification and clustering
Lecture and recitation
10. Week
Linear discrimination, Classification trees, Hierarchical clustering
Lecture and recitation
11. Week
K-clustering
Lecture and recitation
12. Week
Multidimensional scaling
Lecture and recitation
13. Week
Functional PCA, functional classification
Lecture and recitation
14. Week
Functional clustering
Lecture and recitation
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
40
Homework / Term Projects / Presentations
2
20
Final Exam
1
40
Program Outcomes
PO-1
Have scientific research in mathematics and computer science in the level of theoretical and practical knowledge.
PO-2
On the basis of undergraduate level qualifications, develop and deepen the same or a different areas of information at the level of expertise, and analyze and interpret by using statistical methods
PO-3
Develop new strategic approaches for the solution of complex problems encountered in applications related to the field and unforeseen and take responsibility for the solution.
PO-4
Evaluate critically skills acquired in the field of information in the level of expertise and assess the learning guides.
PO-5
Transfer current developments in the field and their work to the groups inside and outside the area supporting with quantitative and qualitative datas as written, verbal and visual by a systematic way.
PO-6
Use information and communication technologies with computer software in advanced level.
PO-7
Develop efficient algorithms by modeling problems faced in the field and solve such problems by using actual programming languages.
PO-8
Respect to social, scientific, cultural and ethical values at the stages of data collection related to the field, interpretation, and implementation.
PO-9
To solve problems related to the field, establish functional interacts by using strategic decision making processes.
PO-10
Establish and discuss in written, oral and visual communication in an advanced level by using at least one foreign language.
Learning Outcomes
LO-1
1. Understanding very variable regression.
LO-2
2. Understanding various techniques used in the field of mathematics.
LO-3
3. Having detailed knowledge about classification and clustering.
