BTEC Level 5 Unit 25 Machine Learning (H/618/7438) Assessment Brief 2026
| University | Pearson Qualifications |
| Subject | Unit 25 Machine Learning (H/618/7438) |
Unit 25 Machine Learning Assessment Brief 2026
| Qualification | Pearson BTEC Levels 4 and 5 Higher Nationals in Computing |
| Unit Number | 25 |
| Unit Title | Machine Learning |
| Unit code | H/618/7438 |
| Unit type | Unit level 5 |
| Unit level | 5 |
| Credit value | 15 |
Introduction
Machine learning is the science of getting computers that have the ability to learn from data or experience to solve a given problem without being explicitly programmed. It has been around for many years, however it has become one of the hottest fields of study in the computing sector. Machine learning is in use in several areas such as predictive modelling, speech recognition, object recognition, computer vision, anomaly detection, medical diagnosis and prognosis, robot control, time series forecasting and many more.
This unit introduces students to the basic theory of machine learning, the most efficient machine learning algorithms and practical implementation of these algorithms. Students will gain hands-on experience of getting these algorithms to solve real-world problems.
Topics included in this unit are: the foundations of machine learning, types of learning problems (classification, regression, clustering etc.), taxonomy of machine learning algorithms (supervised learning, unsupervised learning, reinforcement learning), machine learning algorithms (decision tree, naïve Bayes, k-nearest neighbor, support vector machine etc.).
On successful completion of this unit, students will understand the concept of machine learning and machine learning algorithms. They will have gained hands-on experience in implementing algorithms using a programming language such as C/C++, C#, Java, Python, R, or a machine learning tool such as Weka, KNIME, Microsoft AzureML. As a result, students will develop skills such as communication literacy, critical thinking, analysis, reasoning and interpretation, which are crucial for gaining employment and developing academic competence.
Learning Outcomes
By the end of this unit students will be able to:
LO1 Analyse the theoretical foundation of machine learning to determine how an intelligent machine works
LO2 Investigate the most popular and efficient machine learning algorithms used in industry
LO3 Develop a machine learning application using an appropriate programming language or machine learning tool for solving a real-world problem
LO4 Evaluate the outcome or the result of the application to determine the effectiveness of the learning algorithm used in the application.
Essential Content
LO1 Analyse the theoretical foundation of machine learning to determine how an intelligent machine works
What machine learning is:
Definitions of machine learning.
Core terminologies of machine learning.
Types of learning problems:
Classification, regression, optimisation, clustering.
How machine learning works, including supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, deep learning.
LO2 Investigate the most popular and efficient machine learning algorithms used in industry
Machine learning algorithms and appropriate programming languages or tools:
Introduction to programming languages or tools. Introduction to the language or tool.
A quick tour of the language or tool.
Investigating the mathematical background of machine learning with the programming language or tool:
Formulas, functions, descriptive statistics and graphs, probability.
Investigate the machine learning algorithm and demonstrate using the programming language or a tool:
k-nearest neighbor, support vector machine, linear regression, decision tree, naïve Bayes, k-means clustering.
LO3 Develop a machine learning application using an appropriate programming language or machine learning tool for solving a real-world problem
Problem definition:
Investigate and characterise the problem in order to better understand the goals of the project.
Data analysis:
Understand the available data (rows, columns, classes data range etc.).
Data preparation:
Separate the data as training sets and testing set in order to better expose the structure of the prediction to modelling algorithms.
Implement the algorithm:
Implement the algorithm with an appropriate programming language or tool, train the model using training data set, present results.
LO4 Evaluate the outcome or the result of the application to determine the effectiveness of the learning algorithm used in the application
Improving models’ accuracy:
The cause of poor performance in machine learning is either overfitting or underfitting the data.
Underfitting situations: underfitting happens when a model is too simplistic, usually with less data and is unable to establish an accurate relationship of the variables, causing a high error rate on training and new data.
Overfitting situations: overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Learning Outcomes and Assessment Criteria
| Pass | Merit | Distinction |
| LO1 Analyse the theoretical foundation of machine learning to determine how an intelligent machine works |
LO1 and LO2 |
|
| P1 Analyse the types of learning problems.
P2 Demonstrate the taxonomy of machine learning algorithms. |
M1 Evaluate the category of machine learning algorithms with appropriate examples. | D1 Critically evaluate why machine learning is essential to the design of intelligent machines. |
| LO2 Investigate the most popular and efficient machine learning algorithms used in industry | ||
| P3 Investigate a range of machine learning algorithms and how these algorithms solve learning problems.
P4 Demonstrate the efficiency of these algorithms by implementing them using an appropriate programming language or machine learning tool. |
M2 Analyse these algorithms using an appropriate example to determine their power.
|
|
| Pass | Merit | Distinction |
| LO3 Develop a machine learning application using an appropriate programming language or machine learning tool for solving a real-world problem |
LO3 and LO4 |
|
| P5 Prepare training and test data sets in order to implement a machine learning solution for an appropriate learning problem.
P6 Implement a machine learning solution with a suitable machine learning algorithm and demonstrate the outcome. |
M3 Test the machine learning application using a range of test data and explain each stage of this activity.
|
D2 Critically evaluate the implemented learning solution and its effectiveness in meeting end user requirements. |
| LO4 Evaluate the outcome or the result of the application to determine the effectiveness of the learning algorithm used in the application | ||
| P7 Discuss whether the result is balanced, underfitting or overfitting.
P8 Analyse the result of the application to determine the effectiveness of the algorithm. |
M4 Evaluate the effectiveness of the learning algorithm used in the application. | |
Recommended Resources
Textbooks
Bell, J. (2014) Machine Learning: Hands On for Developers and Technical Professionals. 1st edn. Wiley.
Flach, P. (2012) Machine Learning: The Art and Science of Algorithms that Make Sense of Data. 1st edn. Cambridge: Cambridge University Press.
Kirk, M. (2014) Thoughtful Machine Learning: A Test-Driven Approach. O’Reilly Media.
Links
This unit links to the following related units:
Unit 15: Fundamentals of Artificial Intelligence (AI) & Intelligent Systems
Unit 46: Robotics.
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