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Artificial Intelligence

From symbolic AI and statistical learning to deep networks, transformers, LLMs and production model operations.

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1 · Origins & foundations

How the field began and the maths it runs on.

  1. 1. What Artificial Intelligence Actually IsDefining AI, its 1950s genesis, the symbolic era and why statistics eventually won.
    8m · 3 MCQs
  2. 2. The Maths You Actually NeedLinear algebra, calculus, probability and the shapes that flow through a model.
    10m · 3 MCQs

2 · Classical machine learning

Supervised and unsupervised learning that still runs most production systems.

  1. 3. The Supervised Learning WorkflowFeatures, labels, train/validation/test splits and the bias–variance trade-off.
    9m · 3 MCQs
  2. 4. Core Algorithms: Regression to Gradient BoostingLinear and logistic regression, trees, ensembles and why boosting still wins on tabular data.
    11m · 3 MCQs
  3. 5. Unsupervised Learning & RepresentationsClustering, dimensionality reduction and embeddings as the bridge to deep learning.
    9m · 3 MCQs
  4. 6. Evaluating Models HonestlyAccuracy traps, precision/recall, ROC-AUC, calibration and fairness checks.
    9m · 3 MCQs

3 · Deep learning

Neural networks, training dynamics and the vision/sequence architectures.

  1. 7. Neural Networks from First PrinciplesPerceptrons, activations, forward and backward passes, and what depth buys you.
    10m · 3 MCQs
  2. 8. Training Dynamics That Actually MatterOptimisers, learning-rate schedules, normalisation, regularisation and mixed precision.
    10m · 3 MCQs
  3. 9. Computer Vision & Convolutional NetworksConvolutions, the ImageNet moment, modern backbones and vision transformers.
    9m · 3 MCQs

4 · Transformers & LLMs

Attention, pretraining, alignment and how modern language models behave.

  1. 10. Sequences: RNNs, LSTMs and the Attention BreakthroughWhy recurrence hit a ceiling and how attention removed it.
    10m · 3 MCQs
  2. 11. Inside the TransformerTokenisation, embeddings, self-attention, multi-head projections and the decoder stack.
    12m · 3 MCQs
  3. 12. How Large Language Models Are BuiltPretraining, scaling laws, instruction tuning, RLHF/DPO and the resulting behaviour.
    11m · 3 MCQs
  4. 13. Inference, Decoding and HallucinationSampling parameters, KV caching, quantisation and why models state falsehoods confidently.
    10m · 3 MCQs

5 · Applied & production AI

Prompting, fine-tuning, RAG, evaluation, serving and safety.

  1. 14. Prompt Engineering as a DisciplineStructure, few-shot examples, chain-of-thought, structured output and prompt injection.
    9m · 3 MCQs
  2. 15. Fine-Tuning, LoRA and When Not ToFull fine-tuning vs parameter-efficient methods, dataset design and the decision tree.
    10m · 3 MCQs
  3. 16. RAG and Agentic SystemsChunking, vector search, hybrid retrieval, reranking, tool use and agent loops.
    11m · 3 MCQs
  4. 17. MLOps: Serving, Monitoring and DriftFrom notebook to endpoint: packaging, registries, CI/CD, observability and retraining.
    10m · 3 MCQs
  5. 18. Safety, Ethics and GovernanceBias, privacy, evaluation harnesses, red-teaming and the regulatory landscape.
    9m · 3 MCQs

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