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AI Algorithms: Classification, Regression, and Clustering Explained
This third episode of the ISACA Advanced in AI Audit (AAIA) exam prep series tours the algorithm families that power modern AI — what an algorithm actually is, how engineers control it, and the distinct supervised, unsupervised, and reinforcement learning approaches you will assess in the field. It also covers core enterprise AI terminology and the training pitfalls every auditor must watch for.
What this episode covers
- What an algorithm is and the role of hyperparameters in controlling how it learns.
- The main supervised learning models auditors are expected to recognize.
- The leading unsupervised learning approaches for surfacing structure in unlabeled data.
- Reinforcement learning algorithms and how they scale from simple matrices to neural networks.
- Essential enterprise AI concepts including large language models, prompts, foundation models, NLP, and sentiment analysis.
- The training pitfalls of underfitting and overfitting and why both must be tested before production.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What is an algorithm and what are hyperparameters?
An algorithm is a set of step-by-step instructions designed to solve a specific problem or execute a task. Hyperparameters are configuration settings a human engineer adjusts before training begins, such as the learning rate or how complex a decision tree can grow. Incorrect hyperparameters will derail the learning process and produce unreliable predictions.
What are the four supervised learning models in the AAIA syllabus?
The four supervised models are linear regression, which fits a straight line to data; logistic regression, used for binary classification; tree-based models, which create branching decision structures ending in leaf nodes; and support vector machines, which draw a decision boundary called a hyperplane to separate classes of data.
What is the difference between Q-learning and Deep Q networks?
Q-learning is a model-free reinforcement learning algorithm that stores expected utility values in a matrix called a Q-table, which works well in small static environments. Deep Q networks replace that table with a deep neural network so the system can handle high-dimensional, constantly changing environments and calculate Q-values for all actions simultaneously.
What is the difference between underfitting and overfitting?
Underfitting happens when a model is too simplistic to capture the patterns in the training data, so it performs poorly during both training and testing. Overfitting happens when a model learns the training data too precisely, memorizing noise and outliers, so it performs flawlessly in training but falls apart on new real-world data.
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Reference: This article is based on concepts discussed in AI Algorithms: Classification, Regression & Clustering Explained.