ISY503 - Intelligent Systems Programming - Computer Science Assignment Help

Assignment Task

Learning Outcomes

The 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 it to the different areas of Natural Language Processing, Speech Recognition, Computer Vision and Machine Learning.

Task Summary

This individual assessment provides you 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 in this task, there will be opportunity to apply normalisation techniques. You should follow the task instructions set out in the Google lab to setup and run the various libraries and environments as well as loading the dataset. The instructions will take you through various tasks including identifying different applicable ML models, appropriate hyperparameter 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.

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