The fifth session of the Library’s R series will give an overview of Machine Learning (ML) methods and some popular algorithms. Then we will work with some hands-on case studies to get your own feel for using ML. With this introduction accomplished, the learner will be poised to dive into specific disciplinary questions in the future. Attending sessions 1🔗, 2🔗, 3🔗, and 4🔗 is strongly recommended.  

Topics discussed: 

• What ML is (and isn’t) 

• Classifiers: k-nearest neighbor, naive Bayes, decision trees 

• Black-box methods: artificial neural networks (ANNs), support vector machines (SVMs) 

• Clustering with k-means 

Can't make it to this workshop? You can always contact us with your questions about this and other digital scholarship topics.

This workshop is part of our Coding and Computational Methods Series.