Reference no: EM133922047 , Length: word count:2000

Artificial Intelligence and Machine Learning

Assessment - Machine Learning Project

Task
Develop a real-world Machine Learning or AI project plan/proposal based on the learnings from the subject.

Assessment Description

This assessment seeks to simulate a real-world task that you may have to undertake in the future. Therefore, the assignment is non-prescriptive and requires you to pose a relevant, small, creative and significant problem to solve that could result in benefits to the organisation of choice. You can perform your analysis using Orange or Python.

In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps this organisation needs to take to enable Machine Learning and/or AI for data- driven decision-making. You are required to analyse a sample data set to demonstrate expected AI/ML outcomes. Get expert-level assignment help in any subject.

You need to be familiar with the organisation and industry (e.g., where you have worked or are working, a future start-up company), NOT an organisation such as Amazon/Boeing/Qantas etc.

Well-reasoned use of Generative AI is encouraged. However, generic and irrelevant content will be heavily penalised in the marking.

The report should address:
Why AI would help this organisation given its current operations
What Machine Learning techniques you would recommend
An example of the predictive model using sample data
Deployment considerations for the model
The benefits for the organisation are clearly articulated with estimates of expected revenue/profits or Return on Investment

Assessment Instructions

You will be asked to produce a report and video for this assessment.

PART A:

By Week 11 identify a company and industry you are familiar with that would benefit from Machine Learning/AI. Define a business problem that can be solved using Supervised Machine Learning - Classification in the chosen company (binary or multi- class). Find a sample dataset suitable to solve the business problem defined. Note:

The application needs to be based on Machine Learning/AI (not some other aspect of analytics). Do not select a regression, forecasting, or reinforcement learning task.
Focus on a single, well-defined (small) application.
Sample datasets may be sourced from:
an organisation you work in
public repositories
Open government data

The company, business problem, and dataset must be validated by your workshop facilitator before you proceed with other steps.

By Week 12 draft some preliminary points about the report in class. You are encouraged to consider the current mode of operation, possible inefficiencies, available data and how this data may be used to provide efficiencies based on the concepts and techniques covered in the subject. Think of yourself as a consultant or a founder.

Describe and/or demonstrate (preferable) using techniques from weeks 10 and 11 on how GenAI, Quantum Machine Learning and Reinforcement Learning could enhance your solution.

Include a list of references that are directly related to the content. Each reference needs to be linked to at least one specific point in the content of your assessment. It is expected that you will have at least six relevant references.

Upload the files that contain your predictive modelling workflow (in Orange, Python) to the file submission Dropbox provided on the assessment page. No marks will be awarded for the assessment unless the report, dataset, and software (Orange, Python) workflow files have been submitted.

PART B:

Record yourself doing the video quiz using Zoom screen share. Written answers and oral answers are required. You must provide your written answers in your Google Doc report (after the references section). You are responsible for ensuring your computer, internet connection, and Zoom work properly before taking the quiz. You must share your screen and have your camera on when taking the quiz. Carefully read the instructions available on the assessment page.

There are four sections: (1) business problem identification, (2) data collection, (3) machine learning implementation, and (4) improvements. For each section, the student will have 4-5 minutes to provide answers (written and spoken) to all the questions within the section. Don't forget to paste your answers in your Google Doc report. Make sure you answer only the questions asked. You do not need to repeat the questions when talking. The total allocated time for the video quiz is 25 minutes. After 25 mins, the quiz will stop automatically. Only one attempt is allowed.