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日期：2023-12-13 08:02

Coursework 2: Ensemble Learning of Supervised and Unsupervised Learning Algorithms for Classification Task
In this coursework, you will be performing an image classification experiment using an ensemble model of both supervised learning and unsupervised learning on a specific dataset. This coursework is worth 70% of the module mark. You are required to pick a model of your choice from unsupervised model whereas from the supervised model you are to use the artificial neural network (ANN) model. Also, for the artificial neural network implementation you must have atleast two hidden layer and then build upon more layers systematically. The goal is to make a good prediction or decision while checkmating different performance evaluation metrics. Throughout the task, you will document the steps involved, including data preprocessing, data split, model construction, model training, and model testing. You will also analyze and present your results using various performance evaluation metrics such as loss, accuracy, ROC curve, recall, precision, confusion matrix, and f1-score. The assignment aims to assess your ability to apply machine learning concepts to real-world scenarios, encourage critical thinking and problem-solving skills, and enhance your scientific writing and presentation abilities.
Learning Outcomes
1.Evaluate and articulate the issues and challenges in machine learning, including model selection, complexity and feature selection
2.Demonstrate a working knowledge of the variety of mathematical techniques normally adopted for machine learning problems, and of their application to creating effective solutions
3.Critically evaluate the performance and drawbacks of a proposed solution to a machine learning problem
4.Create solutions to machine learning problems using appropriate software.

Dataset
This coursework is designed for you to work independently and to ensure the uniqueness of you report in order to avoid collision.  At most, two students from the same class group will work on the same dataset. The dataset will be randomly allocated to you and you will have to download the dataset from the link provided to you.  You must use and stick to the dataset assigned to you. Any violation to this instruction will result to a reduction of 20 points because the aim is to encourage students to work independently and avoid collision and cheating.

Machine Learning and Evaluation
For this coursework, you will program and use ANN algorithm of supervised learning and any other unsupervised algorithm of your choice to build an ensemble learning model. The network implementation of the ANN must have atleast two hidden layer to begin with.

Experiments must at least show:
?The training and test error
?The comparison of different hidden layer sizes for the ANN model.
?The entire experiment should be submitted as jupyter notebook script (.ipynb) which can be reproduced.
?Bonus points are given for the implementation of more ANN layers, and meaningful pre-processing steps. Also, considering further deeper analysis of the observed performance, specific errors or other performance evaluation metrics such as precision-recall, confusion matrix, etc or experimental evaluation of any other relevant parameters will be awarded further bonus points.

Report structure and assessment (70% of module mark)
1)Brief Introduction:  (10%)
a)Give a succinct explanation of the classification problem and how it connects to real-life problems.
b)Explain what the dataset is about and what its contents are, what relevant size characteristics are.
c)Describe the ensemble learning model that consists of both unsupervised and supervised learning approaches and any potential parameters.
d)Summarize the ways in which the ensemble model can be used with the dataset.

2)Implement and document the ensemble learning model and the training algorithm (20%)
a)Build your code up systematically step by step and test. Provide evidence of that process.
b)You are highly encouraged to define the custom loss and accuracy functions rather than using in-built functions. The use of in-built loss and accuracy functions is not allowed.
3)Realize and describe an experiment that evaluates the classification error rate for the ensemble model using your dataset. Use appropriate illustrations and diagrams as well as statistics. (20%)
a)Make sure you have one successfully learning parameter set first, and start to explore systematically from there. Pay particular attention to finding an appropriate learning rate first.
b)This experiment can be conducted without a full back-propagation implementation as long as the forward propagation and the learning of the output layer works.

4)Bonus points for additional features of the ensemble model or experiment see above. (5%). Also, student will get an extra bonus point for proposing a new custom loss function supported by mathematical equation and observable evidence in the model performance. (5%)  

5)Write a brief conclusion on the results and compare to results to other algorithms utilized with the same dataset from the homepage or good articles. Explain possible current limitations of your solutions and possible further strategies to improve on the results (10%)

Submission

Submit your report following the report structure provided above. Include step-by-step descriptions of the tasks you performed and the results obtained during the experiment. Ensure that your report is well-organized, clearly written, and includes all the necessary evaluation metrics and graphs as specified in the coursework requirements. The submission deadline is week 12, December 2023, by 16:00. Late submissions may incur penalties of up to 10 marks reduction, so make sure to plan your work accordingly. Failure to submit your coursework will result to Zero Mark. In the case of exceptional circumstances, contact the Award Administrator in advance.

Submission Format:
The coursework assignment submitted should be compressed into a .zip or .rar file, the following files should be contained in the compressed file：
?A report as a Microsoft Word document.
File name format: ‘Student ID_MLCoursework2_Report.docx’
?A .zip or .rar file containing the report experiments: all the program’s sources, including the code, graphs, model architecture, results, and diagrams from the experiments. All implementation source code must be submitted as a Jupyter Notebook script (.ipynb) for easy reproducibility.  Your final zipped folder should be submitted digitally to the student website.
File name format: ‘Student ID_MLCoursework2_Files.zip/rar

Instructions for Referencing
There are two ways of citation in Computer Science: IEEE or ACM. We recommend using IEEE style. IEEE citation style is used primarily for electronics, engineering, telecommunications, software engineering, computer science, and information technology reports. The three main parts of a reference are as follows:
1.Author’s name listed as first initial of first name, then full last.
2.Title of article, patent, conference paper, etc., in quotation marks.
3.Title of journal or book in italics

Each reference number should be enclosed in square brackets on the same line as the text, before any punctuation, with a space before the bracket.
Examples of in-text citation:
“. . .end of the line for my report [1].”
“The theory was first put forward in 1987 [2].”
“Scholtz [3] has argued. . . .” “For example, see [4].”
“Several recent studies [3, 4, 15, 22] have suggested that. . . .”

Reference
[1] S. Bhanndahar. ECE 4321. Class Lecture, Topic: “Bluetooth can’t help you.” School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, Jan. 9, 2008.
Note:
Make sure to start the coursework early, as it involves several tasks that require time and effort. Seek help from the tutor if you encounter any difficulties during the process. Good luck with your image classification experiment and report writing!

This is an individual coursework. The university rules on academic conduct including collusion and plagiarism apply.