Classical ML Algorithms

In this episode, we explore the classical machine learning algorithms that shaped the early foundation of modern AI. These algorithms came long before deep learning became dominant, yet they remain powerful, widely used, and essential to understanding how learning systems work at a conceptual level.We begin by looking at the problems early researchers were trying to solve: prediction, classification, pattern discovery, and making sense of data in a world where computational resources were limited. Classical ML emerged as a collection of intuitive, mathematically grounded techniques designed to learn from data without relying on hand-crafted rules.Throughout the episode, we unpack the core intuition behind the most influential classical algorithms—without going into heavy math or formal theory. Instead, we use simple analogies and everyday examples to show why these algorithms became popular, how they work conceptually, and where they still play an important role.This episode covers:What “classical machine learning” refers to and why it mattersWhy early AI researchers turned to statistical and pattern-based approachesHow supervised algorithms like linear regression, logistic regression, k-nearest neighbours, decision trees, and support vector machines make predictionsHow unsupervised methods like k-means clustering, hierarchical clustering, and PCA uncover structure in dataThe assumptions, strengths, and limitations built into these algorithmsReal-world applications where classical ML still outperforms or complements modern deep-learning systemsHow classical ML techniques continue to influence model design, evaluation, and pre-deep-learning pipelinesWhy classical ML remains foundational for anyone working with artificial intelligence todayThis episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.Sources and Further ReadingRather than listing individual books or papers here, you can find all referenced materials, recommended readings, foundational papers, and extended resources directly on our website:👉 https://adapticx.co.ukWe continuously update our reading lists, research summaries, and episode-related references, so check back frequently for new material.

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