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COMP1801 Machine Learning
Task
Learning Outcome:
1. Demonstrate knowledge and understanding of the major approaches to machine learning.
2. Understand the different types of learning algorithms.
3. Critically evaluate the merits and limitations of machine learning approaches and algorithms and evaluate the choice of algorithms for specific real-world contexts and requirements.
4. Apply machine learning algorithms to selected real-word problems in practice and understand the processes involved in their deployment.
1. Practical Implementation (complete code, trained model and data to replicate the results). The source code must be error-free (i.e. no debugging necessary to run). Minimum requirements:
- Coherent implementation (no errors, sound training pipeline),
- Appropriate metric (Do not confuse metrics for classification, regression, clustering, dimensionality reduction, etc.)
- Sound evaluation (comparison with trivial baselines, such as random flipping coin, outputting the most frequent label, outputting the previous value in time series, and existing machine learning methods that can be applied to the topic.).
2. Written Report (4 pages of a detailed description of the steps taken in carrying out the project):
- Must follow the template uploaded on the Moodle website.
- Should include references (citing other work) where appropriate (when images, data, code, or any other resources have been used from other sources)
- For document structure, please follow the template.
The coursework marking will be based on the report, in principle. The submitted source codes are used, for example, to verify whether the description in the report reflects the implementation and the appropriateness of the implementation but do not have a specific ratio in marking. Even if the implementation is of good quality, it will be ignored if it is not explained well in the report.
COMP1801 Machine Learning
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Tellus molestie nunc non blandit massa enim nec dui. Tellus molestie nunc non blandit massa enim nec dui. Ac tortor vitae purus faucibus ornare suspendisse sed nisi. Pharetra et ultrices neque ornare aenean euismod. Pretium viverra suspendisse potenti nullam ac tortor vitae. Morbi quis commodo odio aenean sed. At consectetur lorem donec massa sapien faucibus et. Nisi quis eleifend quam adipiscing vitae proin sagittis nisl rhoncus. Duis at tellus at urna condimentum mattis pellentesque. Vivamus at augue eget arcu dictum varius duis at. Justo donec enim diam vulputate ut. Blandit libero volutpat sed cras ornare arcu. Ac felis donec et odio pellentesque diam volutpat commodo. Convallis a cras semper auctor neque. Tempus iaculis urna id volutpat lacus. Tortor consequat id porta nibh.
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