|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.
Administrative assistant: DILMAHOMED BOCUS Bibi Swaleha