Coventry University 7135CEM Assignment Help: A+ Support for Modelling and Optimisation under Uncertainty
- R K Gaur
- Jul 16
- 9 min read
Updated: Sep 4
Are you a Coventry University student struggling with your 7135CEM: Modelling and Optimisation under Uncertainty coursework? You are not alone. This module is one of the most technical and research-heavy units in Computer Science and AI programs. Whether it's Gaussian Processes, Bayesian Networks, Dirichlet Processes, Fuzzy Logic, Evolutionary Computing, or Text Mining, this assignment demands advanced skills in machine learning, optimisation, and programming (Python, MATLAB, R).
That’s where we come in. We provide expert guidance and assignment help for Coventry University students looking to achieve A+ grades in this challenging module.
Student Assignment Brief
The work you submit for this assignment must be your own independent work, or in the case of a group assignment, your own group’s work. More information is available in the Assignment Task section of this assignment brief.
Assignment Information
Module Name: Modelling and Optimisation under Uncertainty
Module Code: 7135CEM
Assignment Title: Written Coursework
Assignment Credit: 15 credits
Word Count (or equivalent): 6000 words +/- 10%
Assignment Type: Percentage Grade (Applied Core Assessment). You will receive an overall grade between 0% and 100% (each task of this coursework is worth 50%). You have one opportunity to pass the assignment at or above 40%.
Assignment Task
Task and Mark Distribution:
This coursework consists of two tasks. You should attempt both and submit one Word or PDF file (or similar) for each task. Each task is worth 50 marks, and the marks breakdown for each task is provided with each task. This coursework contributes 100% to your overall module mark.
Task 1: The Machine Learning Algorithms for Solving Real-World Problems in Regression, Classification, Modelling Data, and Text Mining
Individual Research Paper: 50% of the module mark
Context
During this module, you learned about different advanced machine learning techniques, associated concepts, and applications. We explored the Gaussian process model, which is a computationally efficient method for Regression, Classification, optimization, etc. We also covered Bayesian networks as promising tools for modelling data with complex dependency structures. Finally, you learned how to use Dirichlet Latent processes for unsupervised learning applications, particularly text mining.
In this assignment, you will select an application related to a regression, classification, modelling unstructured data, or text mining problem. You will explore how best to apply the machine learning algorithms to solve it. The selected application for each of the methods mentioned above should have the following features:
Gaussian Process Regression and Classification: The application selected for either 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 defining a threshold on the output variable to create binary or multiple classes first, and then apply the Gaussian process classification on the categorized output.
Bayesian Network: If you choose an application for this method, it must consist of at least eight random variables. The random variables could be all discrete, continuous, or hybrid.
Latent Dirichlet Allocation: There is no restriction on selecting the application to apply the Latent Dirichlet allocation model for topic modelling.
There are some potential projects listed below, which could be studied to get ideas. However, I strongly recommend you come up with your own idea(s) by reviewing these projects and other relevant and recent articles.
This dataset from the UCI repository is quite interesting. The task is to predict the depth in the body (effectively, the depth along the spine) given the properties of a two-dimensional "slice" of the body. The hard part about this problem is that it is actually the output causing the input rather than the other way around. I have not had luck designing a good regression method for this data. Can you do this?
Find a Bayesian interpretation of elastic net regularization and compare this method for regression against "standard" Bayesian regression (with a Gaussian prior) on a dataset of your choosing.
Probabilistic PCA using Gaussian Process is a Bayesian interpretation of the classical PCA algorithm for dimensionality reduction. Implement Gaussian Process based PPCA in Python, R, or Matlab, and compare its performance with other methods (such as "standard" PCA) on a dataset of your choosing.
Bayesian optimization is a very important issue with a wide range of applications. However, this was not fully studied during lectures, but it can be easily implemented using Gaussian Process. The Python codes and some examples can be found here!
The squared exponential covariance is widely used for Gaussian process regression. It is probably used in 90+% of all GP publications. That said, it is widely believed to be "too smooth" for many real-world regression tasks. Compare the squared exponential covariance versus the Matérn covariance on several datasets via Bayesian model selection. How often is the squared exponential the right choice?
Latent Dirichlet allocation (LDA) is a Bayesian method for creating "topic models" of text documents. There are plenty of interesting text datasets available (e.g., DBpedia could be a good resource!). One idea would be to compare the behavior of LDA with other techniques, such as latent semantic analysis. You may be able to get relevant datasets and ideas by visiting the following sites:
This competition site consists of some relevant data, and the relevant ideas could be developed by analyzing this data. Also check datasets in Kaggle competitions.
This website has a fantastic compilation of 100 interesting, relevant datasets from all sorts of application areas.
The creators of libSVM have also compiled a great list of datasets, all in a standardized format. The libSVM codebase also includes libsvmread for reading these in MATLAB.
The UCI Machine Learning Repository is a mainstay in machine-learning research. There is a wide range of datasets there from many different application areas and with many different properties (large, small, high-dimensional, low-dimensional, classification, regression, etc.).
Purpose of the First Task of This Coursework
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 real-life regression, classification, modelling data with complex dependency structures, or text-mining problems.
Demonstrate effectiveness in project teamwork and leadership.
You Will Be Required to:
Work individually, developing a paper/report by considering the following instruction. You need to consider developing at least one common methodology and one individual technique for your analysis.
Consult with your tutor about your project work if needed during the Theta hours.
Your Final Submission on Task 1 Will Include:
A scientific paper (in 6 pages A4, up to 4000 words), written individually based on the experience and the derived results by fitting machine learning methods covered in this module.
You are encouraged to target a certain conference or journal and submit the proposed paper to it. You can either use the template of Machine Learning Journal or any other single-column formats from other relevant journal or conference sites.
FLC Design
Design and implement an FLC for controlling the environmental parameters of an intelligent flat for disabled residents. The system needs to automate the regulation of environmental conditions and user preferences or the operation of assistive equipment, ramps, auto-adjusting furniture, kitchen worktops, HVAC, lighting, or water temperature control. The environment could be based on a room of choice in a small flat. A more ambitious project might consider the aspects of the whole flat, but this is left to your choice.
The environmental parameters to be controlled could be ambient temperature, thermal comfort, and lighting using actuators such as cooling fans, heaters/boilers, blinds, and dimmer switches. You might also consider other parameters such as TV or music volume control, and power down options for electronic devices and heating. Environmental parameters could be controlled based on monitoring sensors such as temperature, humidity, weather conditions, light levels, time of day, level of activity/motion of the user, as well as mood and qualitative indicators, such as user preferences.
More details and the above figure can be found at: Ambient Assisted Living.
FLC Design Considerations
The FLC should be based on determining the inputs and outputs of the system, depending on what control behavior(s) you decide the FLC should implement. Note that, depending on the control behaviors you wish to implement, you can select to use a subset of the input sensors. First, think about the behavior(s) the FLC should control.
Design choices should be made to consider the type and number of fuzzy sets for the inputs and/or outputs of the FLC.
A set of suitable control rules should be defined, which can be experimented with to achieve good control performance of the chosen behavior(s).
The FLC should therefore implement the following:
Consideration of which Fuzzy Inference model to use: Mamdani or Sugeno (TSK) fuzzy models.
Mapping the crisp input and output data into the designed fuzzy sets.
Map input fuzzy sets into output fuzzy sets (for Mamdani model) based on a set of designed rules that capture the desired control behavior of the robot.
Employ appropriate inference operation (rule implication) that handles the way in which rules are activated and combined together (composition and aggregation).
The outputs of the fuzzy inference engine will define a modified output fuzzy set (for Mamdani model) that specifies a possibility distribution of the control actions in relation to activated rules.
Use an appropriate defuzzifier to convert the modified fuzzy outputs into non-fuzzy (crisp) control values that can then be used to set the actuation outputs.
Part 1 – Design and Implementation of the FLC (35 Marks)
Design and implement a demonstrable FLC, which can be a simulated system programmed in Matlab, FuzzyLite, or Juzzy.
Provide suitable evidence of your implementation in the form of diagrams and screenshots of the different components.
Discuss and justify your design decisions for the choice of fuzzy sets - membership functions, fuzzy rules, FLC inference mechanism selected, and defuzzification method that was chosen. Back up your explanations with evidence in the form of appropriate diagrams and screenshots. (10 marks)
Perform analysis of the output behavior of the controller showing the rules activation, controller output, and control surface plots, demonstrating how the controller achieves the specified behaviors in relation to an operational scenario. (7 marks)
Part 2 – Compare Different Optimization Techniques on CEC’2005 Functions (15 Marks)
Choose three functions from the CEC’2005 suite of benchmark functions available here: CEC’2005 Functions. More details about the special session at CEC’2005 can be found here: CEC’2005 Special Session.
Submission Instructions:
Submission arrangement online via AULA/CUMoodle:
Submit before 18:00; late work will receive a mark of zero.
File types and method of recording: Submit a single Word file by putting your outputs for both tasks in this Word file.
Marking and Feedback
Your assignment will be marked by the module team. How will I receive my grades and feedback? Provisional marks will be released on 10/08/2024. Feedback will be provided by the module team alongside grades release on each script submitted to Aula.
Your provisional marks and feedback should be available within [2 weeks (11 working days)].
What Will I Be Marked Against?
Details of the marking criteria for this task can be found at the bottom of this assignment brief.
Assessed Module Learning Outcomes
The Learning Outcomes for this module align with the marking criteria provided above. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task:
Apply supervised and unsupervised learning applications using Gaussian process emulators.
Apply Dirichlet processes for unsupervised learning applications.
Develop the knowledge and skills necessary to design, implement, and apply graphical models to solve real-world applications.
Evaluate the applications of fuzzy systems and their usage in hybrid intelligent systems, in combination with evolutionary computing and other machine learning methods.
Apply evolutionary computing methods to develop solutions for real-world optimization problems and appraise their advantages and limitations.
Assignment Support and Academic Integrity
If you have any questions about this assignment, please see the Student Guidance on Coursework for more information.
Spelling, Punctuation, and Grammar:
You are expected to use effective, accurate, and appropriate language within this assessment task.
Academic Integrity:
The work you submit must be your own, or in the case of group work, that of your group. All sources of information need to be acknowledged and attributed; therefore, you must provide references for all sources of information and acknowledge any tools used in the production of your work, including Artificial Intelligence (AI). We use detection software and make routine checks for evidence of academic misconduct. Definitions of academic misconduct, including plagiarism, self-plagiarism, and collusion can be found on the Student Portal. All cases of suspected academic misconduct are referred for investigation, the outcomes of which can have profound consequences for your studies. For more information on academic integrity, please visit the Academic and Research Integrity section of the Student Portal.
Support for Students with Disabilities or Additional Needs:
If you have a disability, long-term health condition, specific learning difference, mental health diagnosis, or symptoms and have discussed your support needs with health and wellbeing, you may be able to access support that will help with your studies. If you feel you may benefit from additional support but have not disclosed a disability to the University, or have disclosed but are yet to discuss your support needs, it is important to let us know so we can provide the right support for your circumstances. Visit the Student Portal to find out more.
Unable to Submit on Time?
The University wants you to do your best. However, we know that sometimes events happen which mean that you cannot submit your assessment by the deadline or sit a scheduled exam. If you think this might be the case, guidance on understanding what counts as an extenuating circumstance, and how to apply is available on the Student Portal.
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