SELECTED TOPIC IN INDUSTRIAL ENGINEERING (4 Credits)

Learning Outcomes:

On successful completion of this Course, students will be able to: apply core theory of selected topics in industrial engineering disciplines; differentiate selected topics in industrial engineering disciplines in different settings and case studies; analyse the most appropriate methods among selected topics in industrial engineering disciplines; 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 (ANFIS);
11. Enrichment Activity: Discussion – Data and pre-processing data using R programming (Enrichment);
12. Enrichment Activity: Lab – Advance frequent itemset mining and association rule mining (Enrichment);
13. Enrichment Activity: GUEST LECTURE – Business analytics using data science techniques and case study;
14. Enrichment Activity: Consultation – Classification: advanced method; Enrichment Activity: Discussion – Clustering for high dimensional data using R programming;
15. Enrichment Activity: GUEST LECTURE – Data science trend and application and opportunity for research frontiers.