Mock Exam 1 — 20 Marks (2025 Format)

Instructions: 60 minutes total. 5 minutes reading + 55 minutes writing. Double-sided handwritten cheat sheet allowed. No calculators. Attempt ALL questions. Be concise and clear.

Question 1: Data Preprocessing [3 marks]

Consider the following dataset summary with 8,000 samples:

For each of the following cleaning steps, explain whether it makes sense to apply it to this dataset. Briefly justify your answers. [0.5 mark per step, + 0.5 for overall justification]

• (a) Missing value replacement based on most frequent value.
• (b) Missing value replacement based on mean value.
• (c) Removing an attribute.
• (d) Standardisation.
• (e) Outlier removal.

Question 2: Bias-Variance and Design Choices [4 marks]

You trained a neural network with the following settings: 8 hidden layers with ReLU units, 128 neurons per layer, trained for 500 epochs with a batch size of 32 and no regularisation. After training, the training accuracy is 98% and the validation accuracy is 52%.

(a) Diagnose the model's performance in terms of bias and variance. [1 mark]

(b) For each of the following suggestions, explain whether it is likely to improve the validation accuracy and why. [1 mark per suggestion]

• Adding dropout (rate=0.5) to each hidden layer.
• Reducing the number of hidden layers from 8 to 2.
• Training for 1000 epochs instead of 500.

Question 3: Evaluation Metrics [3 marks]

A spam detection model is evaluated on a test set of 2,000 emails (200 spam, 1,800 not spam). The confusion matrix is:

(a) Calculate the accuracy, precision, and recall. [1.5 marks]

(b) Is this model suitable for deployment as a spam filter? Explain your reasoning, considering what each metric means in this context. [1.5 marks]

Question 4: Learning Rate and Batch Normalisation [4 marks]

(a) Explain in your own words what is the momentum mechanism in gradient descent optimisation, and describe its effect on training. [2 marks]

(b) Give two effects of batch normalisation on the training of a neural network. For each effect, briefly explain the mechanism behind it. [2 marks]

Question 5: Transformers and ViT [3 marks]

(a) In a Transformer model, the self-attention mechanism computes Query (Q), Key (K), and Value (V) matrices. Explain what role each plays in the attention computation. [1.5 marks]

(b) Explain why multi-head attention is preferred over a single attention head in Transformer models. [1.5 marks]

Question 6: CNN Architecture [3 marks]

Consider the following CNN architecture:

• Input: [32, 32, 3]
• Conv1: 8 filters, kernel size = 5, stride = 1, padding = 0 (valid)
• MaxPool1: kernel size = 2, stride = 2
• Conv2: 16 filters, kernel size = 3, stride = 1, padding = 1 (same)
• MaxPool2: kernel size = 2, stride = 2
• Fully connected layer: ? inputs → 10 outputs

(a) What is the number of inputs to the fully connected layer? [1 mark]

(b) Show your calculation steps for each layer. [2 marks]

END OF MOCK EXAM 1