ML on Apple ][+
7 hours ago
- #algorithm
- #machine-learning
- #k-means
- K-means is considered a machine learning algorithm and is taught in Stanford's CS229 course as an introductory clustering algorithm for unsupervised learning.
- The article provides a detailed explanation of the K-means algorithm, including its initialization, assignment, and update steps.
- The algorithm partitions observations into k clusters based on the nearest mean (centroid), using Euclidean distance for assignment.
- A practical implementation of K-means is demonstrated using APPLESOFT BASIC on an Apple ][+, including code snippets for initialization, assignment, and update steps.
- The article discusses the simplicity of the math behind K-means, focusing on Euclidean distance and centroid calculation without the need for square roots for comparison purposes.
- Debugging techniques and considerations for the algorithm are mentioned, such as handling outliers and ensuring accuracy by adjusting for label mismatches.
- The process of drawing decision boundaries between clusters is explained, highlighting the challenges and solutions for visualizing these boundaries on a screen.
- The article concludes by suggesting that Expectation Maximization (EM) could be a more powerful alternative for Gaussian-distributed data, hinting at future explorations into more complex algorithms like deep learning.