ADVANCED SOFTCOMPUTING (3 Credits)

Learning Outcomes:

On successful completion of this Course, students will be able to: Explain basic concepts, principles, algorithms, and performance metrics of softcomputing; Analyze computing requirements of a computing problem to be solved using softcomputing algorithm; Implement and analyze softcomputing through experiment to address a pattern recognition problem, e.g. classification, regression, clustering, or forecasting; Publish experiment results using softcomputing methods to address a computing problem in a selected domain that has implication to enhancing the quality of human life.

Topics :

1. Pattern learning from data and feature engineering and Introduction to softcomputing;
2. Neural Networks;
3. Application and special topics in softcomputing;
4. Deep Structure NN (Deep Learning);
5. Support Vector Machine (SVM);
6. Properties of Kernels and tuning kernels Kernel PCA, Kernel PLS;
7. Independent Component Analysis/ Cannonical Correlation Analysis;
8. Fuzzy Logic;
9. Boosting Term project evaluation.