2015.09.24: random forest

2015-09-24: Random Forest

Steven gave an overview of Julia. Ben played Andrew Ng's introduction to machine learning lecture video from his course on Coursera.

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

We then discussed different ideas in machine learning.

• POMDPs for Dummies - partially observable Markov decision processes
• Random forests
• Supervised vs. unsupervised learning
• Classification

Ben showed another video on linear regression. Then Haroldur and Steven explained how support vector machines are basically linear classifiers that add additional dimensions to the data to create complex classification boundaries in the original, lower dimensional space. They also warned how they are notoriously bad at overfitting.

Next we split into groups and learned about random forests (an advanced, over-fitting resistant form of decision trees).

Great resources for BOOSTING your learning about decision trees

• Check out this continued tutorial based on Kaggle's Titanic challenge, including a very excellent discussion on why you want to use decision trees, how they fall short, and what you can do to make them more accurate.

• Everything tree from this github repo from Andrew Ng's Practical Machine Learning

• Wkipedia Decision Tree Learning

Ideas for next time

1. Haroldur suggested Statistics for hackers
2. Both Steven and Haroldur expressed interest in discussin Bayesian methods. Haruldur mentioned Probalistic Programming and Bayesian Methods for Hackers which is a book made up of ipython notebooks.