DAI402 Mathematical Foundations for Machine Learning (F/651/0600) Assignment Brief 2026
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DAI402 Assignment Brief
| Qualification | Level 4 Diploma in Artificial Intelligence (610/3934/2) |
|---|---|
| Unit Code | DAI402 |
| Unit Title | Mathematical Foundations for Machine Learning |
| Unit Reference | F/651/0600 |
| Credits | 20 |
| TQT | 200 |
| GLH | 120 |
Assignment Aim
In this unit students willexplore the essential mathematical principles that form the bedrock of modern machine learning. The unit covers core concepts such as linear algebra, calculus, probability, and statistics, providing a mathematical foundation for understanding machine learning algorithms and techniques. Students will develop the skills needed to translate problems into mathematical models and communicate solutions effectively. Students will quantify business solutions to a given complex dataset.
Learning Outcomes and Assignment Criteria
| Learning Outcomes
When awarded credit for this unit, a learner will: |
Assessment Criteria
Assessment of this learning outcome will require a learner to demonstrate that they can: |
| 1. Understand the role of maths and statistics in Machine Learning.
|
1.1 Explain probability and its importance in business analytics. |
| 1.2 Calculate the probability of specific outcomes in given business scenarios. | |
| 1.3 Classify with reasons, a given data as qualitative or quantitative | |
| 2. Understand statistical methods and tools for data analysis.
|
2.1 Explain the concept of inferential statistics and its role in business decision-making. |
| 2.2 For a given business case, explain a hypothesis that can be used to outline a statistical test to validate it. | |
| 2.3 Interpret the results of hypothesis tests in the context of business analytics. | |
| 3. Be able to integrate statistical methods in solving business challenges. | 3.1 Describe linear regression in analytics . |
| 3.2 Compute a simple linear regression model using a provided dataset | |
| 3.3 Interpret the coefficients based on multiple regression analysis on a given dataset. | |
| 3.4 Solve problems using derivatives (Product Rule, Quotient Rule) in business-related scenarios. | |
| 4. Be able to propose business solutions based on inferential statistics results.
|
4.1 Solve integral problems using trigonometric functions, exponentials, and logarithms, and explain their relevance in business contexts. |
| 4.2 Discuss the concept of computational complexity and its implications in data processing. | |
| 4.3 Analyze a given complex dataset and interpret the results using appropriate statistical methods |
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