ECS708P mini-project submission¶
The mini-project consists of two components:
1. Basic solution [6 marks]: Using the MLEnd dataset, build a model that predicts the intonation of a short audio segment.
2. Advanced solution [10 marks]: There are two options. (i) Formulate a machine learning problem that can be attempted using the MLEnd dataset and build a solution model (e.g. identify a numeral in a short sequence). (ii) Create a product that uses the functionality provided by a model trained on the MLEnd dataset (e.g. identify a number based on the identification of individual numerals).
The submission will consist of a single Jupyter notebook for both basic and advanced solution. The uploaded notebook should contain:
• Text cells, describing concisely each step and results.
• Code cells, implementing each step.
• Output cells, i.e. the output from each code cell.
and should have two separate sections for the basic and the advanced solutions.
What should you include in your notebook?
• Title, student name, student ID.
• Brief summary.
• Dataset preparation.
• Dataset visualisation.
• Preprocessing.
• Model(s) description.
• Training and validation tasks.
• Performance evaluation (accuracy, confusion matrix, ROC curve, etc).
• Conclusions.
How will we evaluate your submission?
• Conciseness in your writing (10%).
• Correctness in your methodology (30%).
• Correctness in your analysis and conclusions (30%).
• Completeness (10%).
• Originality (10%).
• Efforts to try something new (10%).
Suggestion: Why don’t you use GitHub to manage your project? GitHub can be used as a presentation card that showcases what you have done and gives evidence of your data science skills, knowledge and experience.
1 Basic solution¶
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2 Advanced solution¶
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