Assessment 1 Information
| Subject Code: | TECH3200 | ||
| Subject Name: | Artificial Intelligence and Machine Learning in IT | ||
| Assessment Title: | The Algorithms and Applications of AI/ML | ||
| Assessment Type: | Side Deck | ||
| Word Count: | 600 Words | /13 Slides | (+/-10%) |
| Weighting: | 20% | ||
| Total Marks: | 20 | ||
| Submission: | My KBS | ||
| Due Date: | Week 5 | ||
Your Task
Your first assessment requires you to apply the concepts learnt during the first four weeks of the subject, as well as researching the supplied references in this document.
Assessment Description
The slide deck is an individual Power Point presentation explaining some concepts learnt related to AI/ML. You are expected to research the supplied references and review workshop slides to develop a good understanding of the different types of ML algorithms and the applications, especially how they are going to support the business.
Thelearningoutcomesyouwilldemonstrateinperformingthisassessmentincludes:
| LO 1: | Evaluate artificial intelligence algorithms in information technology |
| LO 2: | Analyse machine learning and common algorithms |
Assessment Instructions PowerPoint Structure
You are required to create an individual slide deck with dot points and illustrations as per the following:
- The title of the assessment, your name, student ID and date.
- Characteristics of industry 4.0
- How industry 4.0 differs from industry 3.0
- Reading:
- Workshop 1
- See week 1 reading
- Briefly describe what AI and ML are respectively with an example for each
- The relationship between AI and ML
- Reading:
- Work shop 2
- See weeks 1 and 2 reading
- Supervised learning
- What the common algorithms are
- Provide an example of how supervised learning is being used in business
- Unsupervised learning
- What the common algorithms are
- Provide an example of how unsupervised learning is being used in business
- Major difference between Supervised learning and Unsupervised learning
- Reading:
- Workshop 3 and 4
- See weeks 3 and 4 reading
- Research and contrast three Python libraries that are use din Machine Learning
- Reading:
- See weeks 5 and 7 reading
- Add your references (at least 5) in this slide using any professional and consistent styling.
Slide 13 Reference
Slide 11-12 Python Libraries in Machine Learning
Slide 6–10 Types of Machine Learning
Slide 4–5 Fundamentals of AI and ML
Slide 2-3 Industry 4.0
Slide 1: Cover page
Submission Instructions
- Name your document “Assessment 1_[Student ID]”
- Save it as a PPT or PDF document format
Assessment Marking Guide
| Criteria | F (Fail) 0–49% | P (Pass) 50–64% | C(Credit) 65 – 74% | D(Distinction) 75 – 84% | HD (High Distinction) 85 – 100% | Mark |
| Industry 4.0 | Characteristics of industry 4.0 are not described and no illustration of the difference between industry 4.0 and 3.0 |
Characteristics of industry 4.0 are not clearly described and limited illustration of the difference between industry 4.0 and 3.0 with limited understanding of the concept referring to the supplied articles |
Characteristics of industry 4.0 are reasonably good described and some illustration of the difference between industry 4.0 and 3.0 with understanding of the concept referring to the supplied articles |
Characteristics of industry 4.0 are well described and illustration of the difference between industry 4.0 and 3.0 with understanding of the concept referring to the supplied articles |
Characteristics of industry 4.0 are excellently described and clear illustration of the difference between industry 4.0 and 3.0 with well understanding of the concept referring to the supplied articles |
/3 |
| Fundamentals of AI and ML |
Poor or no description of AI and ML with no analysis and justification of the relationship between AI and ML. Provide poor or no example with the applications in business for each AI and ML |
Reasonably ok description of AI and ML with limited analysis and justification of the relationship between AI and ML. Provide limited examples with the applications in business for each AI and ML with evidence of research and understanding of the concepts |
Good description of AI and ML with reasonable analysis and justification of the relationship between AI and ML. Provide examples with the applications in business for each AI and ML with evidence of research and understanding of the concepts |
Very good description of AI and ML with complete analysis and justification of the relationship between AI and ML. Provide clear examples with the applications in business for each AI and ML with evidence of research and understanding of the concepts | Excellent description of AI and ML with comprehensive analysis and justification of the relationship between AI and ML. Provide clear examples with the applications in business for each AI and ML with strong evidence of research and understanding of the concepts | /4 |
| Types of Machine Learning | Poor or no analysis of supervised and unsupervised learning algorithms and provide poor or no examples with the applications in business for each. Poor or no contrast and analysis of the difference | Reasonably ok analysis of supervised and unsupervised learning algorithms and provide limited examples with the applications in business for each. Limited contrast and analysis of the difference | Good analysis of supervised and unsupervised learning algorithms and provide clear examples with the applications in business for each. Good contrast and analysis of the difference |
Very good analysis of supervised and unsupervised learning algorithms and provide clear examples with the applications in business for each. Very good contrast and analysis of the difference |
Excellent analysis of supervised and unsupervised learning algorithms and provide clear examples with the applications in business for each. Comprehensive contrast and analysis of the difference |
/6 |
| Python Libraries in Machine Learning | Poor or no analysis of three Python libraries that are commonly used for machine learning and poor or nocontrast among the libraries | Reasonably ok analysis of three Python libraries that are commonly used for machine learning but limited contrast among the libraries | Good analysis of three Python libraries that are commonly used for machine learning and clear contrast among the libraries | Very good analysis of three Python libraries that are commonly used for machine learning and very clear contrast among the libraries | Excellent analysis of three Python libraries that are commonly used for machine learning and comprehensive contrast among the libraries | /4 |
| Presentation and reference |
Poor structure and clarity. No reference, major grammar and spelling issues |
Reasonable structure and headings. 2 references cited. Some grammatical or spelling issues | Good structure and presentation, headings for different slides, 3 references cited. Reasonable grammar and spelling | Very good structure and presentation, headings for different slides, 4 references cited.Grammar and spelling are very good | Excellent structure and presentation,easy to follow, headings for different slides, 5 or more references cited. Excellent grammar and spelling | /3 |
|
Feed back and grades will be released via My KBS |
Total: /20 |
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Please read the policy to learn the answers to these questions:
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