CAI402 Foundation of Machine Learning Assessment Group Presentation | Yoobee

Programme – NZ2860 New Zealand Certificate in Study and Employment Pathways Version 2 (Level 4, 60 Credits)

Course – CAI402 Foundation of Machine Learning (15 Credits)

CAI402  Assessment Group Presentation 

Weighting within the course:  100%

Objective

This course aims to enable ākonga to build on knowledge and skills in machine learning, collaboration, and fundamental programming concepts.

This assessment evaluates your ability to research, select and summarise key concepts in machine learning and automation using credible sources, and to present your findings clearly as a team.

Learning Outcomes (LOs) covered 

LO1 Research and summarise key concepts of machine learning and automation using information from credible sources.

Graduate Profile Outcomes (GPOs) covered

GPO1  Locate, select and analyse relevant information from a variety of sources, and apply this information by working independently and collaboratively, on context-relevant tasks and problems

GPO2 Construct a well-reasoned and researched argument relevant to their chosen field(s) and communicate it, using appropriate modes and media.

Assessment Matrix

Grading

The final grade will be determined by the score achieved in this assessment based on the following table.  In order to meet the requirements of this course, ākonga | learners must achieve a minimum grade of 50% for all assessments. All assessments must be passed independently; marks are not aggregated or averaged across assessments. Ākonga | learners are permitted one attempt per assessment task.

If an ākonga | learner does not achieve a passing grade on the first attempt, they may be provided with one opportunity to re-sit. To be eligible to re-sit a grade between 40 – 49% on the first attempt is required. 50% is the maximum grade awarded for a re-sit.

Please note:

Failure to achieve a passing result after the re-sit may result in non-completion of the course and you may need to re-enrol in the course to progress in the programme of study.

Assessment Instructions

This assessment is an open-book activity, you can use your own course and review notes as well as offline or online resources, such as textbooks or online journals.

You can always ask your tutor if you need further explanation about forming a group or if the instructions are unclear.
Your work should not be plagiarised. Plagiarism includes copying material without acknowledging it, copying from another student, getting another person to help you with your assessment, using material from commercial essays or assignment services, or using AI to create the answers.

The purpose of this assessment is to assess your knowledge. In the event Yoobee suspects collusion, this will be addressed. For more information on plagiarism, please refer to the Student Handbook.

Marks and feedback will be returned within 15 days of the submission date.

By completing and submitting an assessment you are authenticating that the work is original and does not violate plagiarism or copyright law. Authenticity is checked where any breaches of academic integrity are suspected. Please refer to the Student Handbook for further information

Submission Instructions

Please submit the following to your LMS (Learning Management System) by the due date:

Group Presentation
o Video presentation (.mp4 format), 10-12 minutes in length for groups of 2 or 15-18 minutes in length for groups of 3.
o    Copy of the presentation slides in PDF or .pptx format

Assessment Tasks Completion Timeline

Task Description:

Form a group of 2 or 3 to prepare and deliver a presentation exploring machine learning and automation in real life. Each group member must participate equally in the research, and present for at least 5 minutes.

Presentation Structure and Requirements

Your presentation should be delivered showing good presentation principles, including:

• A Title Slide and Introduction:
  Your presentation must start with an introduction which includes:
  o A presentation title, the names and student IDs of all group members, the course name, and submission date.
  o A brief outline of what the presentation will cover.
• Clear speaking, with confident and engaging delivery
• Equal participation for all team members (at least 5 minutes of presentation time each)
• Presentation Structure and Visual Aids:
  o Logical flow of content
  o Clear and uncluttered slides
  o Use of appropriate and relevant visuals (e.g. charts, diagrams etc.) which
  add value to the subject you are discussing.        

Presentation Tasks

1. Research and Understanding of Key Concepts:

i. Explain in your own words what machine learning is.
ii. Compare supervised and unsupervised learning and provide examples of each.
iii. Explain what automation means in this context and how Python can be used to automate tasks relevant to intelligent systems.

2. Application to Real World Industries:

Choose at least two distinct real-world sectors such as healthcare, transport, agriculture, retail, finance, education etc. For each sector, include:

• A description of a specific problem or issue addressed by machine learning and/or AI automation.
• The type and likely source of data, whether it would be labelled or unlabelled.
• An explanation of the machine learning task and why it is appropriate for this industry and problem or issue.
• Explain the likely benefits, limitations and risks in using machine learning and automation in these contexts. Give at least one benefit and one limitation or risk (for example bias, data quality, maintenance or privacy)
• Supporting evidence with at least one cited source for the information and for any datasets mentioned.

3. Reflective summary on research:

• Explain how your group searched for, evaluated and selected information to use for the presentation.
• Describe at least one challenge your group faced in collaborating or communicating to research and create the presentation.
• Explain how you overcame the challenge(s) described.

4. Use of Credible Sources and Referencing:

• Use at least three credible sources to research your topic.
• Evaluate credibility using CARS or CRAAP, including any datasets you use. Summarise key credibility reasons in your notes or slides.
• Use APA 7 in-slide citations for any facts, figures or images that are not common knowledge.
• Include a final APA 7 reference list as a last slide in the presentation.

CAI402  Assessment Marking Rubrics