In this workshop, we will explore Intel(r) Extensions for Scikit-Learn*. See how to leverage optimizations for common machine learning tools in Scikit-Learn. In this hands-on lab, we explore Intel(r) Extensions for Scikit-Learn patching and unpatching - which can be applied from a global to more surgical level to turn on optimizations for as many as 32 optimized functions. Using a patching strategy can ensure that performance is not lost and has big potential for performance gains. 

Agenda: Use Cases, Patching CPU

• Module 1 - Motivation – key algorithm accelerations and test harness testing
  • Patching – many ways to apply patching from global to surgical: learn how to win/win on the acceleration game with patching
• Module 2 – 
  • Explore use cases for key algorithms and how to patch I that context, pairwise_distance as a stock portfolio nearest shape finder, 
  • Exploration of KNN on Forest coverage dataset
  • Patching for Unsupervised learning Kmeans on CPU
  • Patching for Supervised learning using SVM – Connect4 dataset
  • Practicum: Optional– Telco Customer Churn dataset – PCA, KMeans, DBSCAN, and more
• Module 3 – Practicum Optional: Image clustering example – PCA, Kmeans, DBSCAN
• Module 4 – Practicum: Optional: Synthesized galaxies collide, KNeighbors Classifier, Random Forest, (stretch goal pair_wise distance)