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Machine Learning Using R

Course Code: IM 458; ST 573  |  Credits: 4  |  Prerequisite: None

Syllabus Details

  • Unit I:(Basics of R and Supervised Regression) Data structure, file handling, graphics and data visualization, programming; managing and understanding data: data description, data processing, dimension reduction; introduction to machine learning; supervised learning - regression methods: least-squares, subset selection, shrinkage.
  • Unit II:(Supervised Classification) Classification: LDA, logistic regression, separating hyperplanes, nearest neighbours; Naive Bayes; tree based classifiers; SVM classifier; neural networks (projection pursuit regression, NN).
  • Unit III:(Unsupervised Learning) PCA, MDS, ICA; market basket analysis using association rules; clustering: K-means, K-medoids and hierarchical; random forests.
  • Unit IV:(Model Assessment) Evaluating model performance: measures of performance for regression (R2, F, PRESS), evaluation for classification problems; resampling methods: leave-one-out and k-fold cross-validation, strengths and pitfalls of cross-validation; bootstrap methods.

Course Learning Objective

  1. Discuss different theoretical methods and practicable techniques for extracting information and knowledge from data.
  2. Apply the basic concepts of machine learning and handle real world problems with large scale data in R.
  1. Hastie, Trevor, Tibshirani, Robert and Friedman, Jerome. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, 2009
  2. Lantz, Brett. Machine Learning with R. Packt Publishing, 2013
  3. James, Gareth, Witten, Daniela, Hastie, Trevor and Tibshirani, Robert. An Introduction to Statistical Learning: With Applications in R. Springer, 2013
  4. Maindonald, John and Braun, John. Data Analysis and Graphics Using R. Cambridge University Press, 2003
  5. Crawley, Michael J. Statistics: An Introduction Using R. Wiley, 2005
  6. Seefeld, Kim and Linder, Ernst. Statistics Using R with Biological Examples. University of New Hampshire, 2007
  7. Vinod, Hrishikesh D. (ed.). Advances in Social Science Research Using R. Springer, 2010
  8. Kleiber, Christian and Zeileis, Achim. Applied Econometrics with R. Springer, 2006
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