Reinforcement Learning (RL) is a learning approach in which an agent improves its behaviour by interacting with an environment and observing the rewards produced by its actions.
Unlike supervised learning, the agent is not given a correct action label for every situation. It must discover useful behaviour through trial, feedback, and repeated interaction.
Reinforcement learning is goal-oriented learning through interaction.
flowchart TD
A[Human Language] --> B[Acquire and Prepare Text or Speech]
B --> C[Analyse Language Structure]
C --> D[Represent Meaning and Context]
D --> E[Model or Reason]
E --> F[Understand, Predict or Generate]
F --> G[Evaluate and Improve]
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
Natural language processing (NLP) is the discipline of building machines that can manipulate human language - or data that resembles human language - in the way that it is written, spoken, and organised.
Probability models uncertainty:
what you don’t know yet, but want to reason about.
Key takeaway:
Probability is a number between 0 and 1 that measures how likely an event is.
The whole topic is about defining events clearly and applying a few core rules consistently.
Probability quantifies uncertainty: a number between 0 and 1.
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
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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
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style J fill:#CE93D8,stroke:#8E24AA,color:#000
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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.
This page develops the core vocabulary for reasoning about intelligent agents: percepts, actions, rationality, performance measures, PEAS and the different properties an environment can have.
Intelligent Agents: Notion of Agents and Environments, Rationality
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.
This module applies parallel and distributed computing to ML algorithms. It examines how to divide computation, combine results, and balance communication against useful work during training.
Module coverage:
Parallel ML Algorithms: CNNs, gradient descent and SGD, SVM, k-means, k-nearest neighbours, decision trees, and random forests.
Distributed Training Strategies: data parallelism, model parallelism, pipeline parallelism, gradient checkpointing, and mixed-precision training.
Use M1: Foundations to interpret complexity and speedup. Continue to M3: Scale-Out Systems for the platforms and communication mechanisms that support larger training runs.
