Probability often changes when we learn new information.
Conditional probability and Bayes’ theorem give a structured way to update beliefs using evidence.
Conditional probability updates probabilities after observing an event.
Bayes’ theorem lets you estimate a hidden cause from observed evidence.
Naïve Bayes turns Bayes’ theorem into a practical classifier by assuming conditional independence of features given the class.
flowchart TD
A[Conditional<br/>probability] -->|foundation| B[Bayes<br/>theorem]
D[Independent<br/>events] -->|implies| C[Independence]
C -->|simplifies| A
E[Prior] -->|with likelihood| B
F[Likelihood] -->|updates| H[Posterior]
G[Evidence] -->|normalises| B
B -->|yields| H
I[Naïve<br/>Bayes] -->|uses| B
J[Naïve<br/>assumption] -->|assumes| C
K[Features] -->|given class| J
L[Class] -->|conditions| J
I -->|predicts| M[Classification]
M -->|selects| L
style A fill:#90CAF9,stroke:#1E88E5,color:#000
style B fill:#90CAF9,stroke:#1E88E5,color:#000
style C fill:#90CAF9,stroke:#1E88E5,color:#000
style D fill:#CE93D8,stroke:#8E24AA,color:#000
style E fill:#CE93D8,stroke:#8E24AA,color:#000
style F fill:#CE93D8,stroke:#8E24AA,color:#000
style G fill:#CE93D8,stroke:#8E24AA,color:#000
style J fill:#CE93D8,stroke:#8E24AA,color:#000
style K fill:#CE93D8,stroke:#8E24AA,color:#000
style L fill:#CE93D8,stroke:#8E24AA,color:#000
style H fill:#C8E6C9,stroke:#2E7D32,color:#000
style I fill:#C8E6C9,stroke:#2E7D32,color:#000
style M fill:#C8E6C9,stroke:#2E7D32,color:#000
An intelligent agent connects perception with action. It observes an environment through sensors, uses the information it receives to decide what to do, and affects the environment through actuators.
Develop the core vocabulary for reasoning about intelligent agents: percepts, actions, rationality, performance measures, PEAS and the different properties an environment can have.
flowchart TD
E[Environment] -->|Percepts| S[Sensors]
S --> A[Agent]
A --> C[Actuators]
C -->|Actions| E
style E fill:#E1F5FE
style S fill:#FFF9C4
style A fill:#C8E6C9
style C fill:#EDE7F6
Think of an agent as a continuous loop: observe → decide → act → observe again. An action may change the environment, so the next observation may be different.
The Multi-Armed Bandit (MAB) problem is the simplest setting for studying decision-making under uncertainty.
An agent repeatedly chooses one of
\( k \)
actions. Each action produces a numerical reward drawn from an unknown distribution. The objective is to maximise the expected total reward over time.
The central challenge is deciding when to exploit current knowledge and when to explore uncertain alternatives.
Parallelisation divides computational work into parts that can execute concurrently. The purpose is to reduce completion time or increase throughput, but the gain depends on how much work is genuinely independent and how much overhead is introduced.
This page covers:
speedup, maximum speedup, and processor efficiency
Amdahl’s Law
data-level parallelism
task-level parallelism
algorithm-specific parallelism
communication, synchronisation, scheduling, and load-balancing overhead
flowchart TD
A[Words and Documents] --> B[Observe Their Context]
B --> C[Represent Them as Vectors]
C --> D[Compare Vector Directions]
D --> E[Estimate Semantic Similarity]
E --> F[Search, Classify, Retrieve or Generate]
style A fill:#E1F5FE
style B fill:#C8E6C9
style C fill:#FFF9C4
style D fill:#EDE7F6
style E fill:#E1F5FE
style F fill:#C8E6C9
March 12, 2026