Machine Learning (Cambridge (CIE) A Level Computer Science): Revision Note
Exam code: 9618
Neural networks & deep learning
What is machine learning (ML)?
Machine learning is a type of artificial intelligence (AI) that allows computers to:
Learn patterns from data
Improve performance without being explicitly programmed
Instead of following fixed rules, machine learning systems:
Analyse large amounts of data
Identify patterns or trends
Make predictions or decisions based on that data
ML is used in: spam filters, voice assistants, recommendation systems, fraud detection
What are artificial neural networks (ANNs)?
Artificial neural networks (ANNs) are algorithms inspired by the structure of the human brain
A neural network is made up of layers of nodes (neurons) connected by weighted links
Each neuron receives input, processes it, and passes the result to the next layer
The network adjusts the weights based on errors in output (using algorithms like backpropagation)
How ANNs help machine learning
ANNs are powerful because they:
Automatically learn from experience, even with complex or unstructured data
Improve their accuracy with more data and training
Can solve problems that are too complex for rule-based programming
Examples: handwriting recognition, speech recognition, image classification, language translation
Deep learning (DL)
Deep learning is a subfield of machine learning that uses deep (multi-layered) neural networks
The more layers a network has, the more complex patterns it can learn
Especially effective at learning abstract features in images, text, audio, and video
Example: facial recognition apps learn features like eyes, noses, and then full faces through many layers
Reinforcement learning (RL)
Reinforcement learning is another type of machine learning where an agent learns by interacting with an environment
It receives rewards for good actions and penalties for poor actions
Over time, the agent learns an optimal strategy (called a policy) to maximise rewards
Used in: robotics, self-driving cars, game-playing AI (like AlphaGo), industrial automation
Summary: why use these methods?
Method | What it does | Why use it |
|---|---|---|
Machine Learning | Learns from labelled data | Automates decisions with high accuracy |
Artificial Neural Network | Mimics human brain to recognise complex patterns | Excellent for handling messy, high-dimensional data |
Deep Learning | Uses many neural layers to extract features | Performs well with images, language, sound |
Reinforcement Learning | Learns by trial and error with feedback | Useful where there's no labelled data, only success/failure |
Categories of machine learning
Machine learning algorithms are categorised based on how they learn from data
The two main categories are:
Supervised learning
Supervised learning is when the algorithm is trained on a labelled dataset
Meaning the input data has known outputs (answers)
The goal is to learn a function that maps inputs to the correct output
Once trained, the model can make predictions on unseen data
Example:
Input: Email text
Output: Spam or Not Spam
The model learns from thousands of pre-labelled emails
Used for:
Classification (e.g. face recognition, spam detection)
Regression (e.g. predicting house prices)
Unsupervised learning
Unsupervised learning is when the algorithm is given unlabelled data, and it must find patterns or groupings on its own
There are no correct answers, the system explores the data structure
It identifies clusters, trends, or anomalies without any guidance
Example:
Input: Customer purchase data
Output: Groups of customers with similar buying habits
Used for:
Clustering (e.g. customer segmentation, social network analysis)
Dimensionality reduction (e.g. simplifying complex data)
Back propagation & regression
What is back propagation?
Back propagation is a training method used in artificial neural networks to improve accuracy by adjusting the weights of connections
It is a key part of the supervised learning process and works as follows:
Forward pass:
Input data passes through the network layer by layer
The network produces an output (a prediction)
Error calculation
The output is compared to the actual target value
The difference is called the error
Backward pass
The error is propagated backwards through the network
Each layer calculates its contribution to the error
Weight adjustment
The weights of the connections are updated using an algorithm (e.g. gradient descent) to reduce future errors
Why use back propagation?
Helps the neural network learn from mistakes
Makes the model more accurate over time
Allows multi-layer networks to fine-tune all layers, not just the output
Common in: image recognition, speech recognition, language translation
What is regression?
Regression is a type of supervised learning used to predict continuous values (rather than categories)
The aim is to find the relationship between input features and a numerical output
Types of regression:
Type | Description |
|---|---|
Linear regression | Predicts output using a straight-line relationship (y = mx + c) |
Multiple linear regression | Uses multiple input features to predict one continuous output |
Logistic regression | Predicts binary outcomes (e.g. yes/no), despite the name “regression” |
Example: linear regression
Predicting house prices based on square footage:
Input: size of house
Output: estimated price
The model learns a line that best fits the data points
Why use regression methods?
Useful for forecasting, trend analysis, and risk prediction
Interpretable, often used in real-world problems like:
Business analytics
Finance
Healthcare
Worked Example
Supervised and unsupervised learning are two categories of machine learning.
Describe supervised learning and unsupervised learning.[4]
Answer
Supervised learning (Max 3 of 4)
Supervised learning allows data to be collected, or a data output produced, from the previous experience [1 mark]
In supervised learning, known input and associated outputs are given OR uses sample data with known outputs (in training) OR uses labelled input data [1 mark]
Able to predict future outcomes based on past data [1 mark]
Unsupervised learning (Max 3 of 4)
Unsupervised machine learning helps all kinds of unknown patterns in data to be found [1 mark]
Unsupervised learning only requires input data to be given [1 mark]
Uses any data OR not trained on the right output OR uses unlabelled input data [1 mark]
You've read 0 of your 5 free revision notes this week
Unlock more, it's free!
Did this page help you?
