Reference no: EM132480453

7072CEM - Machine Learning - Coventry University

Machine learning algorithms for solving real-world classification and clustering problems

Learning Outcome 1: Examine the fundamental concepts of machine learning, their implementation and application.

Learning Outcome 2: Prepare appropriate preparation of data sets and evaluate the performance of different learning algorithms on these data sets.

Learning Outcome 3: Appraise the different learning methodologies, their associated algorithms and their appropriateness to solve real-world problems.

Learning Outcome 4: Select and apply learning algorithms to various practical scenarios and evaluate their performance.

Learning Outcome 5: Critique trends in the current machine learning developments.

PURPOSE

• Examine the fundamental concepts of machine learning, their implementation and application.
• Perform appropriate preparation of a dataset and evaluate the performance of different learning algorithms on this dataset.
• Gain practical experience in selecting machine learning algorithms for solving a real-life classification or clustering problem.
• Demonstrate effectiveness in project teamwork and leadership.

SECTIONS:
• Abstract
• Introduction (where you introduce the problem along a short literature review of related work; if the literature review is longer, it is recommended to be a section on its own)
• Problem and Data set(s) description (where you describe in detail the problem you want to solve and its significance)
• Methods (where you shortly describe the machine learning methods and/or other methods employed to solve the problem)
• Experimental setup (including data pre-processing, feature selection and extraction)
• Results
• Discussion and Conclusions
• References

Guidelines and milestones:

Please note, the following guidelines are good practice and should lead to better result, but you have the freedom to pick whatever is suitable for your style:

• Working in groups of 1, 2 (or 3), you have to select a real-world classification/clustering problem and one or more appropriate dataset(s) as suggested above.

• You will write a proposal (maximum of 1 A4 page), giving the title of the project, the names of all group members, the description of the problem and the plan of the work. You will need to submit this proposal to your tutor for formative feedback by Wednesday afternoon week 7.

• You will need to investigate and read related work in the next 2 days. By Friday afternoon, week 7, you have to submit an individually written short literature review of your findings in orderto get formative feedback.

• In the following 2 weeks you have to select, implement and apply appropriate machine learning algorithms to the selected problem, performing data pre-processing, if needed, and record the results from the experiments.

• You will receive regular feedback on your progress from the module leader or tutor in the labs in week 7, and in the following 2 weeks via some scheduled meetings with the module leader.

Attachment:- Machine Learning.rar