MACHINE LEARNING (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Explain the fundamental concept of Machine Learning and its various techniques/algorithms; Describe the characteristics of various Machine Learning algorithms and understand how each of them works, including the mathematical principles underlying the algorithms; Apply relevant Machine Learning algorithms according to individual cases/problems and perform evaluation; Analyse the results obtained from Machine Learning experiments from several perspectives; Able to propose suggestions to improve the system performance.

Topics:

1. Introduction to Machine Learning;
2. Data Exploration and Pre-processing;
3. Regression;
4. Classification;
5. Association Analysis;
6. Artificial Neural Networks (ANN);
7. Support Vector Machines (SVM);
8. Clustering and Mixture Models;
9. Recommender Systems;
10. Model Evaluation and Fusion;
11. Enrichment Activity: Lab Session 1 – Machine Learning Using WEKA;
12. Enrichment Activity: Lab Session 2 – Machine Learning Using RapidMiner;
13. Enrichment Activity: Guest Lecture- Topics: diverse;
14. Enrichment Activity: Lab Session 3 – Neural Network with Python;
15. Enrichment Activity: Presentation – Project Presentation;
16. Enrichment Activity: Guest Lecture – Topics: diverse.