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Module Description

 
 
Module Name
Module Code
Lecture Hrs
Practical Hrs
Credits
Data Mining for Actuarial Science
STAT2205(3)
3
0
3

Description
An overview of machine learning and its applications. Machine Learning concepts, its branches and its process. Difference between regression and classification and generative and discriminative models. Supervised Learning: Penalised generalised linear models, Na´ve Bayes classification, classification and Regression Tree, Greedy Splitting, Boosting, Random forests, Artificial Neural Network. Unsupervised Learning: K-means Clustering, Principal Component Analysis. Association Rule using Apriori algorithm. Applications of machine learning techniques using R. Differences in perspectives of statisticians, data scientists and other quantitative researchers. By the end of this module, the student will be required to submit an assignment on a data mining project using R.
Faculty Department
FACULTY OF SOCIAL SCIENCES AND HUMANITIES Department of Economics and Statistics
Contact Details
Administrative assistant: DILMAHOMED BOCUS Bibi Swaleha
Telephone: 4037400
Email: s.dilmahomed@uom.ac.mu
 

 


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