Subject
Mathematics
Program
Master of Science (MSc)
Location
Côte-des-Neiges
Instruction mode
Credits
3
Description
This course introduces the fundamental concepts in data analysis and statistical learning, as well as the basics of predictive modeling.
This course introduces basic and some advanced methods in unsupervised learning (e.g. dimensionality reduction, cluster analysis) and supervised learning (e.g. parametric model, trees and random forests, boosting). Examples of applications in management illustrate the use of these methods.
Themes covered
- Basics of supervised and unsupervised learning.
- Dimensionality reduction techniques (principal components and factor analysis).
- Hierarchical and non-hierarchical cluster analysis.
- Basics of predictive modeling: Sample division cross-validation bootstrap.
- Parametric models for predictive modeling.
- Association rules (market basket analysis).
- Basic recursive partitioning methods: CART random forest variable importance boosting trees.
- Other topics: Support vector machines nearest neighbor methods basic survival analysis missing data.
Important notes
Course in French : MATH 60603
This courses is not credited in the "Intelligence d'affaires" specialization.
Cours mutuellement exclusif(s) : MATH 60600(A) et MATH 60602 Vous ne pouvez pas vous inscrire à ce cours si vous avez postulé ou réussi un de ces cours ou si un de ces cours fait partie de votre structure: MATH 60600(A) ou MATH 60602.
Cours mutuellement exclusif(s) : MATH 60600(A) et MATH 60602 Vous ne pouvez pas vous inscrire à ce cours si vous avez postulé ou réussi un de ces cours ou si un de ces cours fait partie de votre structure: MATH 60600(A) ou MATH 60602.