Machine Learning
Machine Learning is becoming increasingly important not only in data science, but also bioinformatics. It gives us the opportunity to find hidden patterns in our data and helps us draw predictive conclusions. The goal of this workshop is to learn the definition of Machine Learning, key concepts, categories and simple examples with hands-on practice. Any biologists who want to understand what Machine Learning is and explore how it can be applied to biology are welcome.
The topics that we'll cover are listed below.
- Introduction
- Supervised and Unsupervised
- Bias and Variance
- Cross Validation
- Nearest Neighbors (+ practice)
- K-means clustering (+ practice)
- Decision Tree (+ practice)
Prior experience with R is required. See introductory workshop:
Click here to download the materials.
Before the workshop, please make sure to install R and RStudio on your laptops. Please install following R packages before joining the workshop:
library(class)
library(caret)
library(cluster)
library(factoextra)
library(rpart)
library(rpart.plot)
library(partykit)
library(SamSPECTRAL)
Corresponding commands:
install.packages("class")
install.packages("caret")
install.packages("cluster")
install.packages("factoextra")
install.packages("rpart")
install.packages("rpart.plot")
install.packages("partykit")
BiocManager::install("SamSPECTRAL")
You might have to install this package as well: install.packages("e1071")