LLM - Model

LLM – Large Language Model #

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:

  • Retrieval (RAG)
  • Agents
  • External APIs
  • Memory systems

What makes an LLM special? #

• Built using deep neural networks
• Trained on very large datasets (books, articles, code, web text)
• Can perform many tasks without task-specific training
• General-purpose language understanding, not single-task models

Foundation: Transformer Architecture #

LLMs are based on the Transformer Architecture, which allows models to understand context and long-range dependencies in text.

AI Agents

December 15, 2025

AI Agents #

Also 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)

Retrieval-Augmented Generation (RAG) #

Retrieval-Augmented Generation (RAG) is a system design pattern that improves an LLM’s answers by:

1. Retrieving relevant information from an external knowledge source, and then
2. Augmenting the LLM prompt with that retrieved context before generating the final response.

RAG helps an LLM look things up first, then answer using evidence.

Why RAG is Useful #

RAG is commonly used when:

• Your knowledge is in private documents (PDFs, policies, internal wiki)
• You need up-to-date information (things not in the model’s training data)
• You want fewer hallucinations by grounding answers in retrieved sources
• You want traceability (show “where the answer came from”)

RAG does not change the model weights.
It changes what the model sees at inference time by adding retrieved context.

Decision Tree

Decision Tree #

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).

• Information Theory
• Entropy Based Decision Tree Construction
• Avoiding Overfitting
• Minimum Description Length
• Handling Continuous valued attributes, missing attributes

Information Theory #

Decision trees need a way to measure: “How mixed are the class labels at a node?”

LLM and Prompt Engineering

LLMs and Prompt Engineering #

A Large Language Model extends neural language modelling through much larger datasets, many more parameters, broad pretraining and adaptation to many downstream tasks. Its central operation remains next-token prediction.

Learning Objectives #

• Explain how neural language modelling develops into an LLM.
• Describe the meaning of large, general-purpose and pretrained.
• Explain how a prompt guides generation.
• Distinguish zero-shot and few-shot prompting.
• Compare prompting with model adaptation.

Big Picture #

flowchart TD
    A["Broad Text Data"] --> B["Large-scale Pretraining"]
    B --> C["General Language Model"]
    C --> D["Prompt or Adaptation"]
    D --> E["Task Output"]

    style A fill:#E1F5FE
    style B fill:#C8E6C9
    style C fill:#FFF9C4
    style D fill:#EDE7F6
    style E fill:#E1F5FE

1. From Neural Language Models to LLMs ☆ #

A neural language model learns a conditional probability for the next token:

Prediction & Forecasting

Prediction & Forecasting #

Prediction and forecasting use statistical models to estimate unknown or future values.

In this module, the focus is on correlation, regression, and time series forecasting.

Key takeaway:
Prediction estimates a value using a model.

Forecasting is prediction where the order of time matters.

• Correlation
• Regression
• Time series analysis
• Components of time series data
• Moving average and weighted moving average
• AR model
• ARMA model
• ARIMA model
• SARIMA and SARIMAX
• VAR and VARMAX
• Simple exponential smoothing

Prediction vs Forecasting ☆ #

Overall Workflow #

flowchart LR
    A[Data] --> B[Explore Pattern]
    B --> C[Choose Model]
    C --> D[Train or Fit]
    D --> E[Validate]
    E --> F[Predict or Forecast]
    F --> G[Interpret Error]

    style A fill:#E1F5FE
    style B fill:#C8E6C9
    style C fill:#FFF9C4
    style D fill:#EDE7F6
    style E fill:#C8E6C9
    style F fill:#E1F5FE
    style G fill:#FFF9C4

Correlation ☆ #

Correlation measures the direction and strength of linear relationship between two variables.

Gaussian Mixture Model & Expectation Maximization

Gaussian Mixture Model & Expectation Maximization #

A Gaussian Mixture Model represents data as a weighted combination of multiple Gaussian distributions.

It is commonly used for soft clustering and density estimation.

Key takeaway:
K-means gives hard cluster membership.

GMM gives probabilities of belonging to each cluster.

• Gaussian Mixture Model
• soft clustering
• mixing coefficients
• latent variables
• likelihood and log-likelihood
• Expectation-Maximization algorithm
• E-step and M-step
• responsibilities
• convergence

Motivation ☆ #

Many real datasets are not described well by one Gaussian distribution.

Instance-based Learning

Instance-based Learning #

Instance-based learning is a family of methods that do not build one explicit global model during training. Instead, they store training examples and delay most of the work until a new query arrives.

When a new point must be classified or predicted, the algorithm compares it with previously seen examples, finds the most relevant neighbours, and uses them to produce the answer.

Instance-based Learning covers three linked ideas:

Part-of-Speech Tagging and Hidden Markov Models

Part-of-Speech Tagging and Hidden Markov Models #

Part-of-Speech tagging assigns a grammatical category to each word in a sequence. Because many words can play different grammatical roles, a tagger must use surrounding context rather than examine each word independently.

Learning Objectives #

• Identify common English word classes and Penn Treebank tags.
• Explain why POS tagging is a sequence-labelling problem.
• Describe the Markov assumption.
• Distinguish a Markov Chain from a Hidden Markov Model.
• Explain how an HMM represents POS tagging.

Big Picture #

flowchart TD
    A["Word Sequence"] --> B["Use Context"]
    B --> C["Infer Hidden Tags"]
    C --> D["Tagged Sequence"]

    style A fill:#E1F5FE
    style B fill:#C8E6C9
    style C fill:#FFF9C4
    style D fill:#EDE7F6

1. What Is Part-of-Speech Tagging? ☆ #

A part of speech describes the grammatical role played by a word in a sentence. POS tagging assigns one tag to every word in a sequence.

Support Vector Machine

May 8, 2026

Support Vector Machine (SVM) #

Support Vector Machine (SVM) is a supervised machine learning algorithm used for:

• Classification (most common)
• Regression (SVR – Support Vector Regression)

It connects many earlier ideas:

• classification and decision boundaries
• linear classifiers
• margins
• optimisation
• constrained optimisation
• kernels for non-linear data

SVM is a discriminative classifier.

That means it does not try to model how each class is generated.

Instead, it tries to find the best separating boundary between classes.