People Innovation Excellence

SELECTED TOPIC IN INDUSTRIAL ENGINEERING (4 Credits)

Learning Outcomes:

On successful completion of this Course, students will be able to: To apply core theory of selected topics in industrial engineering disciplines; To differentiate selected topics in industrial engineering disciplines in different settings and case studies; To analyse the most appropriate methods among selected topics in industrial engineering disciplines; To apply the most appropriate methods to solve real engineering problems.

Topics:

  1. An introduction to data science;
  2. Data and pre-processing data;
  3. Data dimensionality reductions;
  4. Frequent itemset mining and association rule mining: An introduction;
  5. Classification technique using Decision tree and Bayes classification;
  6. Classification: advanced method using BBN and SVM;
  7. Classification using Artificial Neural Nework (ANN);
  8. Clustering: Basic concept using K Means;
  9. Advance method in clustering;
  10. Adaptive Neuro Fuzzy Inference System;
  11. Data and pre-processing data using R programming;
  12. Advance frequent itemset mining and association rule mining;
  13. Business analytics using data science techniques and case study;
  14. Classification: advanced method;
  15. Clustering for high dimensional data using R programming;
  16. Data science trend and application and opportunity for research frontiers.

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