UECM3993 Predictive Modelling (February 2025)

The course outcomes are

CO1: Describe the key concept of statistical learning;
CO2: Compare statistical models for prediction and estimation through supervised learning;
CO3: Identify relationship and structures from unlabelled data through unsupervised learning;
CO4: Demonstrate supervised and unsupervised learning with statistical software;
CO5: Interpret results from supervised and unsupervised learning.

Announcements

Week 1: Practical and Tutorial start from Week 1. Tuesday is Thaipusam holiday. A pre-recorded video will be prepared for P1 and P2. You may join the Wednesday 3-4pm class at KB904 if you prefer physical class. Please scan the QR code before Wednesday 4 pm for your attendance.
Week 2: Assignment question is ready. Start forming assignment groups and pick the dataset of interest.
Week 3: All assignment groups should be formed. Those not in an assignment group will not get marks for early formation of assignment groups and will be randomly assigned a group by lecturer.
Week 4: Assignment groups will be finalised and assignment starts.
Week 6: Tuesday is Nuzul Al-Quran holiday. A pre-recorded video will be prepared for P1 and P2. You may join the Wednesday 3-4pm class at KB904 if you prefer physical class. Please scan the QR code before Wednesday 4 pm for your attendance.
Week 8: Online prerecorded videos for lecture, tutorial and practical will be prepared due to Hari Raya Puasa holiday. Please scan the QR codes in MS Teams before Friday for the attendance.
Sample Practical Quiz from an earlier semester
Week 11: Wednesday (23 April) 6pm: Deadline for the submission of assignment report and computer program. This should be the last week for tutorial classes.
Week 12: This should be the last week for practical classes.
Week 12 or Week 13: Oral Presentation will be arranged.
Week 14 Friday or later: Check the coursework marks which will be announced in MS Teams’s 2025.02 channel). Inform the lecturer if there are any mistakes.
Past Year Exam Sample