https://arshadhs.github.io/tags/parallel-machine-learning/Recent content in Parallel Machine Learning on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/038-ml-system-optimisation/m2-distributed-ml-algorithms/040-parallelisation-of-ml-algorithms/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/038-ml-system-optimisation/m2-distributed-ml-algorithms/040-parallelisation-of-ml-algorithms/<h1 id="parallelisation-of-ml-algorithms"> Parallelisation of ML Algorithms <a class="anchor" href="#parallelisation-of-ml-algorithms">#</a> </h1> <p>Machine-learning algorithms expose different kinds of parallel work. The correct decomposition depends on whether independent work occurs across records, features, trees, clusters, kernel entries, or optimisation steps.</p> <p>Course coverage:</p> <ol> <li><strong>Problem decomposition</strong> for parallel machine learning</li> <li><strong>Ensemble methods and XGBoost-style tree ensembles</strong></li> <li><strong>Parallel k-means</strong> assignment and centroid reduction</li> <li><strong>Distributed decision trees and random forests</strong></li> <li><strong>Support vector machine parallelisation</strong> using block and MapReduce-style computation</li> </ol> <h2 id="learning-objectives"> Learning Objectives <a class="anchor" href="#learning-objectives">#</a> </h2> <p>By the end of this page, you should be able to:</p>