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AI3023 Machine Learning Workshop Course Project

Description:

This is a GROUPproject (cach group should have 46 students), which aims at applying machine learing models as well as machine learning techniques (including but not limited to those covered in our lectures) to solve complex real-world tasks using Python and relevant libraries.

Notice: This project should differ from the one you are undertaking in the Machine Leaming Course.

Notice on Deep Learning Models:

You may decide to work on Deep learning models, and since our course mainly focus on machine learning models and techniques, deep learning model not be considered as more superior than other machine leaming models if you just repeat a model that is designed by others. Also, training deep learning models can be very time consuming, so make sure you have the necessary computing resouroes.

Project Requirements:

1. Problem Selection:

• Choose a real-world problem from a domain of interest (e.g, healthcare, finance, image recognition,natural langunge processing, ctc.).
• Describe the problem, including data sources and the type of machine learning model that will be applied (eg., regression,classification, clustering, etc.).

2. Dataset Selection:

• Choose complex or complicated (e.g.high-dimensionality, large volume) datasets from public repositories (e.g,UCI Machine Leaming Repository,Kaggle).
• Ensure the datasets have a sufficient number of samples and features to allow for meaningful analysis and model comparison.
• Should not be a standard dataset (like Iris, MNIST, ete.) commonly used in beginner tutorials.

3.Preprocessing:

• Handle missing data.
• Convert categorical data into numerical(ifnecessary).
• Normalize or standardize data.

4. Exploratory Data Analysis:

• Visunlize the data distribution.
• Discover and interpret possible correlations.
• Identify anomalies or outliers.

5.Model Selection andTraining:

• Each group should select at least 4 machine learning models for comparison,and cach student should implement at least 1 model.
• Proper validation techniques should be implemented (e.g.,cross-validation).
• Hyperparameters tuning using methods like grid scarch or random search.

6. Evaluation:

• Choose relevant metrics for your problem domain.
• Compare the models in terms of:
• Performance (accuracy.precision, ctc.).
• Computational complexity (training time, memory usage).
• Suitability for the dataset (e.g, which model performs best,why).
• Provide a comparison of the models' performances with appropriate visualizations (c.g, bar plots or tables comparing metrics).
• Interpret the results.

Submission Requirement:

Upon completion, each group must submit the following materials:

1.Progress report

a) Abstract
b)Introduction: problem statement, motivation and background of the topic
c) Related works and existing techniques of the topic
d)Methodology
c)Progress/Current Status
f)Next Steps and Plan for Completion

2. Project report, your report should contain but not l imited to the following content:

a) Abstract
b) Introduction: problem statement, motivation and back ground of the topic
c) Related works and existing techniques of the topic
d)Methodology
e)Experimental study and result analysis
f) Future work and conclusion
g) References
h) Contribution ofeach team member

3.Link and description to the Datasets and the implementation code.

4.Your final report should be a minimum of 12 pages and a maximum of 15 pages content check Must Not cxceed 25%

5.For the final report, the similarity check Must Not exceed 20%, and the AI generation

6. Put all files (including: source code, presentation ppt and project report) into aZIP file, then submit it on iSpace.

Deadline:

• Team Information should be subm itted by the end of Week 3.
• The Progress Report should be submitted by the end of Week 10
• The Presentation will be arranged inWecks 14 and 15 of this semester.
• Final Project Report should be submitted byFriday of Week 15(May.23.2025).

Assessment:

In general, projects will be evaluated based on:

• Significance.(Did the authors choose an interesting or a “real" problem to work on, or only a small “toy" problem? Is this work likely to be useful and/or have impact?)
• The technical quality of the work. (i.e., Does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting?Do the student convey nove insight about the problem and/or algorithms?)
• The novelty of the work. (Do you have any novel contributions, c.g., new model, new technique, new method, etc.? Is this project applying a common technique to a well- studied problem, or is the problem or method relatively unexplored?)
• The workload of the project. (The workd oad of your project may depend on but not l imit to the following aspects: the complexity of the problem; the complexity of your method; the complexity of the dataset; do you test your model on one or multiple datasets? do you conduct a thorough experimental analysis on your model?)

Evaluation Percentage:

• Progress Report: 5%
• Final Report: 40%
• Presentation:40%(Each group will have 15-20 minutes for presentation, and each student must present no less than 3 minutes)
• Code:15%

It is YOUR responsibility to make sure:

• Your submitted files can be correctly opened.
• Your code can be compiled and run.

Late submission = 0; Plagiarism (cheating) =F