Context-aware translation drafts. Original English remains available.
Russian celebrates Daria and Adémidé’s family heritage; Igbo celebrates Lékan’s maternal roots.
Translation notes & sourcesSuggest a correction ↗MSC AI & DATA SCIENCE / MODULE 05
Knowledge representation, dimensionality reduction, supervised learning and responsible interpretation.
THE PRACTICE BEHIND THE MODULE
Choosing a technique means comparing its data needs, failure modes and impact on the people represented in the source.
Knowledge representation, dimensionality reduction, supervised learning and responsible interpretation.
FROM THE SOURCE REFLECTION
My account of the learning and its application, distilled from my combined course reflection. The original document is held privately.
About the code: Python code and notebooks for the coded studies are privately stored and maintained by me. I’m happy to answer questions about the methods and their limits; any sharing of an artefact would depend on the permissions and data involved.
MILESTONES IN PRACTICE
Three steps show how the learning moved into an artefact or decision, and where the evidence stops.
Compare knowledge representations and the mechanics of learned models.
Test PCA and digit classification on public teaching benchmarks.
Explore a restricted positive-feedback source while rejecting automated personnel judgments.
SELECTED PORTFOLIO EVIDENCE
Original summaries of my work. Each distinguishes the source, the work's stage and what it can support.
APPLIED STUDY
I traced symbolic reasoning, semantic networks, ontologies and knowledge graphs, considering how representations influence what a system can infer.
Reading the evidence
This is a literature-based synthesis, not a deployed reasoning engine.
APPLIED STUDY
I explored principal component analysis on the breast cancer benchmark, a simple neural-network forward and backward pass, and support vector classification of handwritten digits.
Reading the evidence
These teaching benchmarks demonstrate method selection and evaluation; they are not clinical diagnosis or a production service.
APPLIED STUDY
I structured a feedback source and explored text and classification methods to reveal recurring themes and the limits of positive-only data.
Reading the evidence
Recording habits and opportunity bias make individual ranking or automated reward decisions inappropriate. No person-level output is published.
These summaries do not reproduce academic submissions, restricted datasets, individual records, internal systems detail or private course materials. A design, prototype and deployed service are different stages; each study is labelled accordingly.
Watch my QMUL apprenticeship interview and read the learning reflection — the personal journey behind these methods.
Use the categories to distinguish a capability, an algorithm, a Python package and a delivery step. A mention in a module or case study does not mean that capability is running on this page.
Number of concepts in this guide, not performance or usage measures.
12 concepts shown
On small screens, scroll the table sideways. “Learning context” explains a related idea; it does not mean that method runs on this page. Deep learning is the standard term for “deep ML”.
| Technical term | Technical description / definition | In plain English | How this page uses it |
|---|---|---|---|
| Machine learning (ML)AI Capability | Methods that estimate patterns or parameters from examples to support inference on other inputs.Primary reference ↗ | Learn a pattern from examples, then check whether it helps with new ones. | Learning context ML learns from examples. Read the page-specific methods below to see whether any model runs here. |
| Deep learningAI Capability | Machine learning using neural networks with multiple representation-learning layers.Primary reference ↗ | A layered model learns useful representations; more layers do not guarantee a better answer. | Learning context Deep learning is an AI capability, not a label for every chart. No deep model is implied by this explanation panel. |
| Natural language processing (NLP)AI Capability | Computational methods for representing, analysing or generating human language.Primary reference ↗ | Help a computer work with words, while remembering that context matters. | Learning context Text search, translation, thematic classification and generation are different language tasks; their implementation is identified separately. |
| Sentiment analysisAI Capability | A language-analysis task that estimates an expressed attitude, often as polarity or sentiment categories.Primary reference ↗ | Estimate the tone of words—not the private feelings or personality of their author. | Learning context Sentiment estimates expressed tone. It must not be confused with topic classification or a diagnosis of a person. |
| Data science pipelineData Science Pipeline | An ordered, reproducible sequence for acquiring, validating, preparing, analysing and evaluating data.Primary reference ↗ | A chain from a question and source to an answer someone can check. | Method guide The learning-to-practice path connects framing, preparation, modelling, evaluation and communication. |
| Features and labelsAI/Data Concepts | Features are model inputs; labels are target values or categories used in supervised learning.Primary reference ↗ | What the model sees, and the answer it is learning to estimate. | Learning context Projects define relevant input features and any target labels before choosing an estimator. |
| Held-out evaluationEvaluation & XAI | Assessment on examples excluded from fitting and model selection, according to the declared split.Primary reference ↗ | Keep some answers out of practice so the test means something. | Learning context Unseen and appropriately separated cases help assess whether a learned pattern generalises. |
| Explanation versus causal proofEvaluation & XAI | A model explanation describes selected behaviour or contributions; it does not establish causal effects or full reliability.Primary reference ↗ | A reason for this output is useful, but it is not proof that the model is right. | Responsible use Explain what was actually done and what the available evidence supports. |
| NumPyPython Package | A Python library for multidimensional arrays and numerical operations.Primary reference ↗ | The numerical toolbox behind many Python calculations. | Learning context A numerical Python library; it is not an AI capability or an algorithm by itself. |
| pandasPython Package | A Python library for labelled tabular data manipulation and analysis.Primary reference ↗ | A programmable workbench for cleaning and comparing tables. | Learning context A tabular Python library; a useful preparation tool, not a prediction model. |
| scikit-learnPython Package | A Python library providing estimators, preprocessing tools and model-evaluation utilities.Primary reference ↗ | A toolkit of models and fairer ways to test them. | Learning context A Python library of algorithms and evaluation tools, distinct from each fitted model. |
| Human oversight and consentResponsible AI | People retain appropriate review authority, permission controls and correction routes for data use and outputs.Primary reference ↗ | Keep someone able to question, correct or stop the result. | Responsible use Decision ownership, permission and correction belong throughout the workflow. |
No matching concepts. Choose another category or clear the search.
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