Modelling and Simulation in Data Science and Computing Engineering
Course details
- Coordinator: Florian Rossmannek
- Lecturers: Florian Rossmannek, Juntao Yanga, Xu Quanqingb, Wilson Gohc
- Location: always SPMS-LT4 except final lecture in SPMS-LT3
- Dates: March 4, 11, 18, 25, April 1, 8, 15a, 22a, 29a, May 11c, 13b, 14b, 25
- Time: always 6:30-10pm
Contact information
Assessments
- Quizzes on April 8 and May 25 (each 30% of final grade)
- Group project (40% of final grade)
Project details
- Deliverables: Report and Presentation
- Report deadline: May 22 (23:59pm)
- Submission: Send report via email to coordinator
- Email subject line: [MH6551] GroupLetter - Report submission
- Filename: 2026_GroupLetter_report.pdf
- Presentation and code must not be submitted
- Schedule: Presentations held during final lecture (May 25)
- Presentation: 10 minutes, every group member has to speak
Suggested Repositories
Report formatting guidelines
Content guidelines
- Describe the objective of your project (cover at least two course topics)
- Detail your methodology: (e.g. nature of the data, preprocessing, model or architecture, training procedure, validation and testing methods, etc.)
- Justify why you followed this methodology (ideally with references to the lecture; optionally to textbooks or research papers but not a must)
- Describe the challenges you encountered and how you overcame them
- Describe your use of LLMs and their impact on the project
- Include a contribution/responsibility statement for each group member
- Include visuals in the appendix (e.g. loss curves, accuracy plots, etc.) and reference them in the main report
- Do not include code in the report, the aim of your report is to communicate your understanding of the material
Lecture material
The slides will be uploaded on a rolling basis on
NTU Learn.
The Jupyter notebooks can be accessed online in Google Colab through the links below.
If you want to open them locally on your computer, you can download them as well as the datasets from NTU Learn.
Jupyter Notebooks
References
- M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, Second Edition, 2018, MIT Press, link
- S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Algorithms, 2014, MIT Press, link