Introduction to Machine Learning

Machine learning is becoming increasingly important, not only in data science, but also in bioinformatics. Machine learning gives you the opportunity to find hidden patterns in your data and draw predictive conclusions. In this beginner workshop, you’ll learn the basics of machine learning concepts and approaches, and get hands-on practice with simple examples.

You’ll be introduced to topics including:

• Supervised and unsupervised machine learning
• Training data and test data sets
• Bias and variance
• Cross validation
• Performance evaluation

The hands-on practice will focus on the algorithms:

• K-Nearest Neighbors (KNN)
• K-means clustering
• XGBoost

Prerequisites for demo: Prior experience with R is required, be sure to review the Introduction to R for data analysis workshop.

Visit the workshop site for more details and materials.

This workshop series is made possible through the generous support of Gladstone Institutes, Genentech, UCSF and Gladstone-CIRM SRL.