Hypothesis testing is a statistical decision-making method used to decide whether sample evidence is strong enough to reject an initial assumption about a population.
It connects probability, sampling distributions, confidence intervals, significance levels, and decision rules.
Key takeaway:
Hypothesis testing is not about proving something with certainty.It is about asking:
If the null hypothesis were true, how surprising would this sample result be?
This module considers how to make models practical when memory, compute, latency, and energy are limited. It connects model compression with edge deployment and the trade-offs required to retain useful prediction quality.
Module coverage:
| Chapter | Topic | Availability |
|---|---|---|
| 12 | Model Compression Foundations | Planned |
| 13 | Quantisation and Edge Intelligence | Planned |
| 14 | Pruning and Deep Compression | Planned |
| 15 | Optimisation Landscape and Small Language Models | Planned |
| 16 | Energy-Constrained ML | Planned |
This index records the module scope. Chapter pages can be added here as their source material becomes available.
Neural networks learn useful representations and nonlinear relationships directly from data. In language modelling, they replace discrete N-gram identities with learned word embeddings and use these representations to predict the next word.
flowchart TD
A["Context Words"] --> B["One-hot Inputs"]
B --> C["Embedding Lookup"]
C --> D["Combined Context"]
D --> E["Hidden Layer"]
E --> F["Softmax"]
F --> G["Next-word Probabilities"]
style A fill:#E1F5FE
style B fill:#C8E6C9
style C fill:#FFF9C4
style D fill:#EDE7F6
style E fill:#E1F5FE
style F fill:#C8E6C9
style G fill:#FFF9C4
A neural unit receives input values, multiplies them by learned weights, adds a bias, and applies an activation function.
Machine-learning algorithms expose different kinds of parallel work. The correct decomposition depends on whether independent work occurs across records, features, trees, clusters, kernel entries, or optimisation steps.
Course coverage:
By the end of this page, you should be able to:
December 14, 2025AI models trained on massive datasets to perform a wide range of tasks with minimal fine-tuning.
are large deep learning neural networks
are large AI models trained on massive and diverse datasets (text, images, audio, or multiple modalities).
Contain millions or billions of parameters.
designed to perform a broad range of general tasks
designed for general-purpose intelligence, not a single task.
acts as base models for building specialised AI applications
Large Language Models (LLMs) are advanced AI systems designed to process, understand, and generate human-like text.
They learn language by analysing massive amounts of text data, discovering patterns in:
grammar
meaning
context
relationships between words and sentences
Built on Deep Learning
Implemented using Neural Networks
Based on Transformers
Often combined with tools like:
LLMs are based on the Transformer Architecture, which allows models to understand context and long-range dependencies in text.
December 15, 2025Also referred to as Agentic AI.
AI agents are intelligent systems that can plan, make decisions, and take actions to achieve goals with minimal human intervention.
A common use case is task automation
for example booking travel based on a user’s request.
AI agents typically build on Generative AI and use Large Language Models (LLMs) as the reasoning core.
Agents often interact with tools (APIs, databases, calendars) to complete multi-step workflows.
Retrieval-Augmented Generation (RAG) is a system design pattern that improves an LLM’s answers by:
RAG helps an LLM look things up first, then answer using evidence.
RAG is commonly used when:
RAG does not change the model weights.
It changes what the model sees at inference time by adding retrieved context.
Distributed machine learning is effective only when saved computation exceeds the cost of moving data and synchronising workers. Algorithm design must therefore account for message size, message frequency, barriers, and parameter placement.
Course coverage:
By the end of this page, you should be able to:
A decision tree classifies an example by asking a sequence of questions about its attributes until it reaches a leaf (final decision).
Key takeaway: A decision tree grows by repeatedly splitting the training data into purer subsets using an impurity measure (Entropy / Gini / Classification Error).
Decision trees need a way to measure: “How mixed are the class labels at a node?”