Machine Learning Series — Random Forest

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

Topics:

1. Introduction to Machine Learning (ML)
   1. Big data and Machine Learning
   2. Relate Machine Learning to other disciplines
   3. Machine Learning algorithms
   4. Classification and Regression
2. Random Forest (RF)
   1. Applications of Random Forest
   2. Why Random Forest
   3. Understanding Random Forest
   4. Fundamental concepts – ML
   5. Fundamental concepts — RF
3. Random Forest – How
   1. How to select relevant features — Feature Selection.
   2. How to deal with missing data – Proximity Matrix
   3. How to split the node –node impurity
   4. How to limit over-fitting
   5. How to evaluate the model performance &
4. Lab Exercise:
   1. Feature selection and evaluation
   2. Random Forest Classification
   3. Random Forest Regression

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