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BoKSA

Topics

Topics

Both lists are candidate topics. The number of groups per class depends on class size, so the final list may differ. The topics can also help you prepare for the profile-specific knowledge test that is held twice a semester. For more background on your profile, see the AI Engineer and Creative Technologist profile pages.

Each topic can only be chosen once per class. See First week: choose your topic for how to choose.

AI Engineer

These topics come from the semester's self-study material, which is available for free online:

The foundational knowledge page of your profile has more on the self-study material.

Topic What it covers
Data wrangling, EDA & visualisation NumPy, pandas and Matplotlib as the basis for exploring and preparing a dataset. PDSH: ch. 1, ch. 2, ch. 3, ch. 4
Classical machine learning Decision trees and random forests, linear regression, support vector machines, naive Bayes, and unsupervised methods such as clustering, PCA and manifold learning. PDSH: ch. 5
Foundations & fundamentals of deep learning What deep learning is, the mathematical building blocks, and practical fundamentals: overfitting, generalisation and validation. DLWP: ch. 1, ch. 2, ch. 4, ch. 5
Computer vision: image classification Convolutional neural networks applied to image classification. DLWP: ch. 8
Time series forecasting Forecasting techniques for sequential and time-based data. DLWP: ch. 13
Language models & text classification Representing and classifying text with neural networks, how language models work, and the Transformer architecture behind current LLMs. DLWP: ch. 14, ch. 15

Creative Technologist

These are technical AI themes, not generic design methods. Problem analysis, research through design, evidence-based decisions, prototyping and user testing still matter, but you use them as ways of working within every theme. See the knowledge page of your profile for the foundations behind them.

The external resources are recommendations

The links marked Recommended are optional suggestions to help you get started. You are not required to use them. Use them, or other resources you find, in whatever way helps you and your group.

Topic What it covers
Local language models & human–AI interaction Small local models, prompting, local processing, quality, privacy, conversational interaction and user control. Recommended: Ollama quickstart, Guidelines for Human-AI Interaction
AI sees, hears & interprets Image, sound, pose or hand recognition; training data, labels, error behaviour, context and exclusion. Recommended: ml5.js beginner's guide (The Coding Train), Gender Shades
Trust, uncertainty & transparency in AI interactions How interfaces present AI output, limitations, sources, uncertainty, correction and control. Recommended: Explainability + Trust, Errors + Graceful Failure (Google People + AI Guidebook)
Autonomy & human control in AI systems AI that advises, performs steps or uses tools; consent, boundaries, oversight and recovery after errors. Recommended: Feedback + Control (Google People + AI Guidebook), Building Trustworthy Agents
Data, classification & meaning How systems categorise, score, select or recommend; the assumptions involved and ways to make them visible. Recommended: Datasets Have Worldviews (Google PAIR), Excavating AI
Generative AI as creative & critical material Local text generation and, where feasible, multimodal interaction; visualisation and the influence of generated output on making, choices and authorship. Recommended: Machine Learning for Artists, Introduction to Generative AI and LLMs

The recommended technology baseline for Creative Technologists is:

For topics such as trust, autonomy and human control, the AI Ethics Framework can help you weigh a system against ethical considerations.