AI

Hypothesis Testing

March 12, 2026
AI, Statistics
AI, Statistics

Hypothesis Testing #

Hypothesis testing is a structured way to decide:

Is what we see in a sample just random variation, or is there evidence of a real effect in the population?

Hypothesis Testing topic sits inside inferential statistics: we use a sample to make a statement about a population.

  • Sampling (random and stratified)
  • Sampling distribution and Central Limit Theorem
  • Estimation (confidence intervals and confidence level)
  • Testing hypotheses (mean, proportion, ANOVA)
  • Maximum likelihood (MLE)

Key takeaway: The logic is always the same:

Classification(Linear Models)

AI, ML
AI, ML

Linear models for Classification #

  • categorises data by finding a linear boundary (hyperplane) that separates classes
  • calculating a weighted sum of input features plus bias
flowchart TD
T["Linear<br/>classification<br/>models"] --> P["Perceptron"]
T --> LR["Logistic<br/>regression"]
T --> SVM["Linear<br/>SVM"]

P -->|uses| STEP["Step<br/>activation"]
LR -->|uses| SIG["Sigmoid<br/>+ log loss"]
SVM -->|uses| HNG["Hinge<br/>loss"]

style T fill:#90CAF9,stroke:#1E88E5,color:#000

style P fill:#C8E6C9,stroke:#2E7D32,color:#000
style LR fill:#C8E6C9,stroke:#2E7D32,color:#000
style SVM fill:#C8E6C9,stroke:#2E7D32,color:#000

style STEP fill:#CE93D8,stroke:#8E24AA,color:#000
style SIG fill:#CE93D8,stroke:#8E24AA,color:#000
style HNG fill:#CE93D8,stroke:#8E24AA,color:#000

Discriminant Functions #

Decision Theory #

Probabilistic Discriminative Classifiers #

Logistic Regression #

  • Supervised machine learning algorithm
  • Binary classification algorithm
  • requires data to be linearly separable
  • predicts the probability that an input belongs to a specific class
  • uses Sigmoid function to convert inputs into a probability value between 0 and 1

Key takeaway: Logistic regression predicts $P(y=1\mid x)$ using a sigmoid of a linear score $z=w\cdot x+b$, then learns $w,b$ by maximising likelihood (equivalently minimising log-loss).

Foundation Models

December 14, 2025
AI, ML
AI, ML, Foundation Models, LLM

Foundation Model #

AI 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

LLM - Model

AI, ML
AI, ML, Deep Learning, LLM

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, ML
AI, ANI, AGI, ASI

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)

AI, ML
AI, Generative AI, LLM, 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

AI, ML
AI, ML

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 #

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

Instance-based Learning

AI, ML
AI, ML

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: