7135CEM – Modelling and Optimization Under Uncertainty - IT Assignment Help

Assignment Task

Module Learning Outcomes Assessed:
On completion of this module the student should be able to:

1. Apply supervised and unsupervised learning applications using Gaussian process emulators.
2. Apply Dirichlet processes for unsupervised learning applications
3. Develop the knowledge and skills necessary to design, implement and apply the Graphical models to solve real world applications.
4. Evaluate the applications of fuzzy systems and their usage in hybrid intelligent systems, in combination with evolutionary computing and other machine learning methods.
5. Apply evolutionary computing methods to develop solutions for the real world optimisation problems and appraise their advantages and limitations.

1. Gaussian Process regression and Classification: The application selected for any of these two methods must consist of at least four input variables and a single output variable. You must also implement Gaussian process classification by appropriately define a threshold on the output variable to create a binary or multiple classes first, and then apply the Gaussian process classification on the categorized output.
2. Bayesian network: If you are choosing an application for this method, this application must consist of at least eight random variables. The random variables could be all discrete or continuous or hybrid.
3. There is no restriction on selecting the application to apply the Latent Dirichlet allocation model for topic modelling.

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