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日期：2025-05-24 11:10

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

Coursework Assignment

1 Assignment Overview

This assignment will involve you designing, building, testing and critiquing systems for two applied machine learning tasks.

1. Task 1 (50%): A system for performing spam detection, aka. classifying spam from non- spam in text.

2. Task 2 (50%): A system for performing face alignment, aka. locating facial landmarks in images of people.

This assignment is worth 100% of the grade for this module. It is designed to ensure you can demonstrate achieving the learning outcomes for this module, which are to:

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1.

Determine the applicability of different machine learning models to data found in real- world applications.

Propose designs for simple systems, including appropriate pre-processing, to solve practical problems using machine learning.

Implement and document a computer program that learns and applies machine learning models to realistic data.

Critically evaluate the efficacy of proposed systems and appropriately communicate this analysis.

What to hand in?

A report that comprises a maximum of 10 pages and 3000 words, including captions but excluding references. We expect several pictures, diagrams and flowcharts to be included. Please only use the .zip archive format for your submission.

The report should be written in two sections, one for each task. For each task, you should cover the following points. More detail is provided in Sections 3 and 4 below.

• A summary and justification for all the steps in your system, including preprocessing, choice of features and prediction model. Explaining the system diagrammatically is very welcome.

• Results of your experiments. This should include some discussion of qualitative (ex- ample based) and quantitative (number based) comparisons between different ap- proaches that you have experimented with.

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

• Examples of failure cases in your system and a critical analysis of these, identifying potential biases of your approach.

2. Either .ipynb files or .py files containing annotated code for all data preprocessing, model training and testing.

3. For Task 1: A csv file that contains the predicted labels on the test set of text, found in the csv file (spam detection test data.csv) here. You must use the provided “save as csv” function in the Colab worksheet to process an array of shape (number test data, 1) to a csv file. Please make sure you run this on the right data and submit in the correct format to avoid losing marks.

4. For Task 2: A csv file that contains the face landmark positions on the test set of images, found in the compressed numpy file (face alignment test images.npz) here. You must use the provided “save as csv” function in the Colab worksheet to process an array of shape (number test image, number points, 2) to a csv file. Please make sure you run this on the right data and submit in the correct format to avoid losing marks.

Note! Do not reorder the test data or it will not match up with the test labels / points!

3 Task 1: Spam Detection

3.1 Mark allocation

25 marks will be awarded for writing and presentation and 25 for coding and data analysis.

10 Marks

25 Marks

Accuracy and robustness of spam detection

These marks are allocated based on the performance of the spam detection method. This will be evaluated on the held out test set. The test data (without labels) are provided in the csv file (spam detection test data.csv) here and the error on the predicted labels will be calculated after submission. Marks will be awarded for average accuracy and robustness (based on the confusion matrix of your predictions). Note that only we have the test labels!

Outline of methods employed

Justifying and explaining design decisions for the spam detection. This does not have to be in depth, and we do not expect you to regurgitate the contents of the lecture notes/papers. You should state clearly:

• any text pre-processing steps you have used, and why.

• what text features/representation you have used, briefly describe how they were cal-

culated, and why you chose them.

• what predictions methods you have use; what ML task this corresponds to, the type of model that you have used, and the loss function that your system is trained with.

• design/parameter decisions should be explained and justified.

For top marks, you should clearly demonstrate a creative and methodical approach for designing your system, drawing ideas from different sources and critically evaluating your choices. Explaining using diagrams and/or flowcharts is very welcome.

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

15 Marks Analysing results and failure cases

Critically evaluate the results produced by your system on validation data. You should include quantitative (number based) and qualitative (example based) comparisons between different approaches that you have tried (on a held-out validation set).

3.2

Most important links

Quantitative measures include calculating the confusion matrix of your predictions. Please note that we are interested in your final prediction results, rather than how the cost function changes during training. Please explicitly define any evaluation metrics and ensure they are appropriate for the task.

Contents

Training text and label data

Test text data (without labels)

Colab worksheet with some useful functions

3.3 Where to start?

filetype links

csv file (spam detection training data.csv) link csv file (spam detection test data.csv) link Colab worksheet link

   Text classiciation is covered in lecture 14, so that’s a good place to look for information. Other lectures (e.g., lecture 15) are also helpful.

We have included a very basic Colab worksheet illustrating how to load the data and print random text examples based on their labels. An example print-out is shown in Figure 1.

The simplest approach would be to treat this as a classification problem, where given text data you want to predict the whether or not it is spam.

To follow this approach you will need to consider what natural language pre-processing steps are necessary to obtain suitable features for your predictive model.

Figure 1: Example of non-spam text (label == 0) in the training dataset (spam detection training data.csv).

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

4 Task 2: Face Alignment

4.1 Mark allocation

25 marks will be awarded for writing and presentation and 25 for coding and data analysis.

10 Marks Accuracy and robustness of face alignment

These marks are allocated based on the performance of the face alignment method. This will be evaluated on the held out test set. The test images, without annotations are provided in the compressed numpy file (face alignment test images.npz) here and the error on the predicted points will be calculated after submission. Marks will be awarded for average accuracy and robustness (% of images with error below a certain threshold). Note that only we have the test points!

25 Marks Outline of methods employed

Justifying and explaining design decisions for the landmark finding. This does not have to be in depth, and we do not expect you to regurgitate the contents of the lecture notes/- papers. You should state clearly:

• any image pre-processing steps you have used, and why.

• what image features/representation you have used, briefly describe how they were

calculated, and why you chose them.

• what predictions methods you have use; what ML task this corresponds to, the loss function that your system is trained with, and a description of any regularisation that you may have used.

• design/parameter decisions should be explained and justified.

For top marks, you should clearly demonstrate a creative and methodical approach for designing your system, drawing ideas from different sources and critically evaluating your choices. Explaining using diagrams and/or flowcharts is very welcome.

15 Marks Analysing results and failure cases

Critically evaluate the results produced by your system on validation data. You should include quantitative (number based) and qualitative (example based) comparisons between different approaches that you have tried (on a held-out validation set).

Quantitative measures include measuring the cumulative error distribution (see lecture slides) or using boxplots or other plots to compare methods. Please note that we are interested in your final prediction results, rather than how the cost function changes during training. Please explicitly define any evaluation metrics and ensure they are appropriate for the task.

4.2

Contents filetype links

Most important links

   Training images and points

compressed numpy array (face alignment training images.npz)

 link

 Test images (without points)

compressed numpy file (face alignment test images.npz)

 link

 Colab worksheet with some useful functions Colab worksheet link

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

4.3 Where to start?

Face alignment is covered in lecture 8, so that’s a good place to look for information. Other lectures (e.g., lecture 7) are also helpful.

We have included a very basic Colab worksheet illustrating how to load the data and visualise the points on the face. A visualisation of the average face and points across all training images is given in Figure 2.

The simplest approach would be to treat this as either a regular or a cascaded regression problem, where given an image you want to predict the set of continuous landmark coordinate locations.

To follow this approach you will need to consider what image features are helpful to predict the landmarks and what pre-processing is required on the data. Although you could directly use the flattened image as input, this will not be the optimal data representation for this task.

A better representation would be to describe a set of locations, either evenly spaced across the image, or in some more useful pattern (think about where in the image you might want to calculate more information) using a feature descriptor, such as Scale-Invariant Feature Transform (SIFT). These descriptions can then be concatenated together and used as input into a linear regression model. Note that you do not need to use the keypoint detection process for this task - rather the descriptors should be computed at defined locations (hint: look at sift.compute() or similar) to create a representation of the image that is comparable across the dataset.

Figure 2: Illustration of the 0-indexed (counting from 0 as you would in Python) locations of the points on the average face. For example, if we wanted to find the nose, that’s index 2 so we would look up points[2,:], which would give you the x and y coordinates.

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

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General Points on the report

Read things! Provide references to anything you find useful. You can take figures from other works as long as you reference them appropriately.

Diagrams, flowcharts and pictures are very welcome! Make sure you label them properly and refer to them from the text.

All plots should have correctly labelled axis and the font sizes must be readable in A4 page format.

All figures (including plots) should have descriptive captions.

Notes on using Colab

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Either you can complete this project using Google Colab, which gives you a few hours of comput- ing time completely free of charge, or you can use your personal/lab machine. The lab machines are fairly powerful, so if you need more computing resource then try those!

If you are using Google Colab, try and familiarise yourself with some of its useful features.

To keep your saved models, preprocessed data etc. you can save it to Google drive following the instructions here. You can also directly download a file you make in Colab using the code below:

from google.colab import files files.download(filename)

If you refactor code into extra .py files, these should be stored in your google drive as well, or on Box such that they are easy to load into your Colab worksheet.

7 What software functionality can I use?

You are not allowed to use generative AI tools (e.g., ChatGPT, Deepseek, etc.) to solve these tasks or write your report.

You are not allowed to use library functions that have been written to directly solve the tasks you have been given, i.e. text classification and face alignment. You cannot use the dlib or mediapipe face alignment tools or anything that provides similar functionality. Also, face detection is not required on this data.

You are free to use fundamental components and functions from libraries such as NLTK, OpenCV, numpy, scipy, scikitlearn to solve this assignment, although you don’t have to. Here, fundamental components refers to things like regression / classification models and pre-processing / feature extraction steps and other basic functionality.

In terms of tools and frameworks, it’s absolutely fine to use convolutional neural networks (CNNs) or recurrent neural networks (RNNs) if you want to. The best packages would be either TensorFlow (probably with Keras) or PyTorch. If you use such an approach you should be sure to document how you chose the architecture and loss functions. A well justified and high performing deep learning approach will receive equivalently high marks as if you had built it any other way.

In terms of sourcing additional labelled data, this is not allowed for this assignment. This is because in real-world commercial projects you will typically have a finite dataset, and even if

Drs. J. Senk & P. Wijeratne: Applied Machine Learning @ University of Sussex – Spring 2025

there are possibly useful public datasets available, their license normally prohibits commercial use. On the other hand data augmentation, which effectively synthesises additional training examples from the labelled data that you have, is highly encouraged. If you use this, please try and add some text or a flow-chart of this process in your report.

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Top Tips for Success

Refer to lecture slides and labs - all the information is in there to complete these tasks!

Remember Occam’s razor: complexity should not be added unnecessarily. The more complicated your system the more things to explain/justify etc.

Start with a simple achievable goal and use that as a baseline to test against. Keep track of early models/results to use as points of comparison.

Remember that even if it doesn’t work well, having a go at both tasks is worthwhile. We’re only looking for simple solutions and your explanation of your system design.

Think about things that you have learned about in Applied Machine Learning. Dimen- sionality reduction could be helpful. Overfitting and outliers may be an issue, and you should consider using methods to minimise this.

For Task 2: You don’t need to work at very high resolution to get accurate results. Partic- ularly when doing initial tests, resize your images to a lower resolution images. Make sure you also transform your training points so they are in the same geometry as the image (i.e., if you half the size of the image along both axes, then make sure to half the (x,y) position of the training points too). For your predicted points, make sure these are all at the same resolution as the original images.