EEEMEM0024 APPLIED ML FOR ENGINEERING – 2025/26
Coursework Assignment
1 INTRODUCTION
Briefly introduce the context and problem statement with main hypotheses.... This is an example of a citation [1].
2 DATASET DESCRIPTION AND ML MOTIVATION
Describing the dataset, its complexity, and motivation for using ML. Why do you need to use ML? Advantages and disadvantages compared against other approaches (ULO 1)
3 MODEL CHOICE
Justify the choice of a model comparatively in the context of the given problem (ULO 2)
Describe the model architecture and set of hyperparam- eters (ULO 2)
4 DATA PRE-PROCESSING
Method to improve and set the dataset up for the analysis with the chosen ML method (ULO 1)
5 OPTIMISATION PROCEDURE
Description of the optimisation procedure and metrics used for the evaluation of the method (ULO 3)
Eq. (2) is an example of an equation in LATEX:
E = mc2 (1)
where E is the energy, m denotes the mass, and c represents the speed of light. And Eq. (2) is an example of an equation in LATEX:
E = mc2 (2)
where E is the energy, m denotes the mass, and c represents the speed of light.
6 RESULTS
Reporting results from both optimisation procedure and optimal model (ULO 3).
Table 1 and Fig. 1 are examples of how to introduce both figures and tables.
TABLE 1
Example of table.
7 DISCUSSION
Appraisal of the model results and its direct application, including repeatability (ULO 3)
How would you improve this model to get significantly better results? (ULO 1–2)
Fig. 1. Example of an arbitrary figure.
8 CONCLUSIONS
Concise summary followed by 2-3 main conclusions, prefer-ably in a list:
• First conclusion.
• Second conclusion.
ACKNOWLEDGEMENTS
Disclose any use of Generative AI, what you used it for, and the model/bot you have used.
REFERENCES
[1] P. J. Antsaklis et al., “Neural networks for control sys- tems,” IEEE Transactions on Neural Networks, vol. 1, no. 2, pp. 242–244, 1990.