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Subject Code and TitleISY503 Intelligent Systems
AssessmentProgramming Task
Individual/GroupIndividual
LengthCode + Manual (500 words +/- 10%)
Learning OutcomesThe Subject Learning Outcomes demonstrated by successful completion of the task below include a) Determine suitable approaches towards the construction of AI systems.   c)   Apply knowledge-based or learning-based methods to solve problems in complex environments that attempt to simulate human thought and decision-making processes, allowing modern society to make further advancements.   e)  Apply the foundational principles of AI learnt throughout the course and apply them to the different areas of Natural Language Processing, Speech Recognition, Computer Vision and Machine Learning.
SubmissionDue by 11:55 pm AEST Sunday end of Module 8 (Week 8).
Weighting35%
Total Marks100 marks

Task Summary

This individual assessment provides you with an opportunity to explore the impact of applying various Machine Learning techniques on a dataset in a sandbox environment. You will complete the Programming Exercise from Google that will introduce you to modelling in the Machine Learning world. Note that this exercise is limited to exploring the application of Linear Regression in great detail, however, the feature engineering, transformations and hyperparameter tuning involved in applying different implementations of the regression algorithm are investigated. There is an emphasis on understanding the impacts of various feature transformations as well. Although a simple data set has been provided for this task, there will be an opportunity to apply normalisation techniques.

You should follow the task instructions set out in the Google lab to set up and run the various libraries and environments as well as load the dataset. The instructions will take you through various tasks including identifying different applicable ML models, appropriate hyperparameters and feature transformation exploration. While writing your own models, think outside the box and see if a custom ML model can be made. As there is no “one right answer” to this task, the assessment is seeking to help you explore the impacts of various possible options to further your own understanding. The task instructions and rubric outline in detail what each grade assigned to students will demonstrate.

The assessment also requires you to write a manual of approximately 500 words, explaining the models and ML techniques utilised, what impact they had on the data exploration and visualisation task and provide an evaluation of their efficiency. Once again, this is an exploration task and your analysis and conclusions of the effectiveness of various models you’ve investigated will be the subject of the marking criteria.

Task Instructions in Programming Task in ISY503 Intelligent Systems

You need a Google account to do this assessment. You can create a free Google account here: https://myaccount.google.com/.

Once created, you need to navigate to the Google created lab: “Intro to Modelling” here: https://colab.research.google.com/github/google/eng- edu/blob/master/ml/fe/exercises/intro_to_modeling.ipynb?utm_source=ss-data- prep&utm_campaign=colab-external&utm_medium=referral&utm_content=intro_to_modeling

In addition to following the instructions outlined in the lab, you must:

  • Implement a possible solution to each of the tasks outlined in the lab
  • Add appropriate comments to your code created, following machine learning best practices for clean coding: https://towardsdatascience.com/clean-machine-learning-code- bd32bd0e9212
  • Identify various different models that would be appropriate to use as alternatives for the tasks presented by the lab by varying hyperparameters and features. There is also an opportunity for you to create your own custom model by using different regressor functions

within TensorFlow. For more details, see: https://www.tensorflow.org/tutorials/customization/basics

  • Familiarise yourself with the assessment rubric to understand how the various assignment grades are assigned.
  • Produce a manual of 500 words in length outlining:
  • The answers to the questions posed in each of the tasks within the lab.
    • The choice of models you made during your assessment including the various hyperparameters you chose and feature engineering performed for the appropriate task.
    • An analysis of the various models created and an evaluation of their efficiency.

Referencing

It is essential that you use the appropriate APA style for citing and referencing research. Please see more information on referencing here http://library.laureate.net.au/research_skills/referencing

Submission Instructions

Submit your code as a Jupiter notebook (.ipnyb) and manual (as a .docx) via the Assessment 2 link in the main navigation menu in ISY503: Intelligent Systems. The Learning Facilitator will provide feedback via the Grade Centre in the LMS portal. Feedback can be viewed in My Grades.

Academic Integrity Declaration

Please be aware of the Torrens University Australia Academic Integrity Policy and Procedure viewable online at http://www.torrens.edu.au/policies-and-forms

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