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Machine Learning and AI Models: Supervised, Unsupervised, and Reinforcement Learning
This second episode of the ISACA Advanced in AI Audit (AAIA) exam prep series walks through how machine learning systems actually consume information and learn, from the foundational role of data through the main training paradigms and the architectures that power modern deep learning. It gives auditors the vocabulary needed to assess algorithmic design quality, surface inherent bias, and design governance controls.
What this episode covers
- What machine learning is and how it fits inside the broader AI hierarchy.
- The role of labels in training data and why tagging enterprise datasets is so difficult.
- Supervised learning — including the split between regression and classification, plus the semi-supervised variant.
- Unsupervised learning — how systems uncover hidden patterns without human instruction.
- Reinforcement learning — trial-and-error reward systems and the autonomy risks they introduce.
- Neural network architectures and the specialized variants auditors commonly encounter.
- When a neural network crosses the threshold into deep learning, and what that means for explainability.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the three main machine learning paradigms?
The three fundamental training paradigms are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses datasets tagged with correct answers, unsupervised learning hunts for hidden patterns in raw untagged data, and reinforcement learning uses a trial-and-error reward system to master dynamic environments.
What is the difference between regression and classification?
Both are categories of supervised learning. Regression always produces a quantitative output, meaning a hard continuous number such as a delivery time in minutes. Classification always produces a qualitative output, meaning a category or descriptive trait such as labeling a part structurally sound or defective.
Why is labeling data so difficult for organizations?
Labeling is difficult for three reasons: the sheer volume of records makes manual tagging astronomically expensive and time consuming, some categorizations are complex or ambiguous and require costly specialists, and in novel research areas there is no ground truth because the correct answers do not exist yet.
When does a neural network qualify as deep learning?
A neural network qualifies as deep learning when it contains more than three total layers — an input layer, an output layer, and at least two or more hidden layers in between. Deep learning models can autonomously extract and transform features without a human engineer pointing out which traits to look for.
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Reference: This article is based on concepts discussed in Machine Learning & AI Models: Supervised, Unsupervised, Reinforcement.