Register to attend at the bottom of this page. A Webex link will be emailed after filling out the the registration form.

Instructor: Yun-juan (Janet) Chang

Topics:
1. Introduction to Machine Learning (ML)

• Big data and Machine Learning
• Relate Machine Learning to other disciplines
• Machine Learning algorithms
• Classification and Regression

2. Understanding Random Forest (RF)

• Applications of Random Forests
• Why Random Forests
• The Random Forest Algorithm
• Fundamental concepts – ML, RF

3. Implementing Random Forest

• Feature Importance and Feature Selection
• Dealing with missing data, and imbalanced data
• Best split of the node–node impurity
• Over-fitting and underfitting
• The model performance
• The model interpretability

4. Lab Exercise

We will use health data combined with gene expression data to build random forest models, predicting output variables. Both classifier and regressor will be addressed.

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