MACHINE LEARNING (2/1 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – explain fundamental machine learning concepts and differentiate between AI, Machine Learning, and Deep Learning; LO2 – perform data pre-processing data using Python, including feature engineering; LO3 – apply the best machine learning model to solve problem(s) using Python; LO4 – apply a variety of machine learning techniques in software engineering contexts.

Topics:

1. Introduction to AI in Software Engineering;
2. Data Preparation;
3. Regression;
4. Classification;
5. Tree-based Methods;
6. Clustering;
7. Quiz;
8. Fundamentals of Statistical Machine Learning;
9. Machine Learning Process;
10. Supervised Learning;
11. Resampling Methods;
12. Model Selection and Regularization Techniques;
13. Tree-based Methods;
14. Support Vector Machines (SVM);
15. Unsupervised Learning;
16. Deep Learning and Neural Networks;
17. Multiple Testing;
18. Ethical Considerations and Potential Biases in Machine Learning Applications;
19. Project Presentation.