Overview of Machine Learning

1. Overview of Machine Learning RPI Robotics Lab Spring 2011 Kane Hadley

2. Agenda • What is Machine Learning? • Some techniques • Simple Implementations • Implementations for complex problems

3. A computer program learns from an experience E with respect to task T and some performance measure P if its performance on T as measured on P improves with experience E. ~Tom Mitchell

4. Supervised Learning • Aims to find a function f(x) -> y • Learns by correcting itself to match that function • Examples • Support Vector Machines • Artificial Neural Networks

5. Support Vector Machine

6. Artificial Neural Network

7. Unsupervised Learning • Attempts to find a good representation for a given data set • Examples • K-Means Clustering • Self Organizing Maps

8. K-Means Clustering • Tries to find K clusters for a data set. • Clusters are found by approximating centroids for each cluster.

9. Self Organizing Maps • Attempts to fix the space of the map to a given data set.

10. Reinforcement Learning • Goal is to maximize a given reward function. • Reward is calculated using utilities given to each state in the world.

11. Genetic Algorithms • Form of optimization. • Starts with a population and fitness function • At each time step evaluate the fitness of each member, remove the lowest fitness member, breed the two members with the highest fitness and mutate.

12. Videos • Stanford Copter • Little Dog

13. Criticisms • Slow • Requires lots of data • Not necessarily optimal

14. References • http://www.csie.ntu.edu.tw/~cjlin/libsvm/ • http://www.karlsims.com/evolved-virtual-creatures.html • http://ccsl.mae.cornell.edu/research/golem/index.html