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Overview of Machine Learning

This overview from the RPI Robotics Lab outlines fundamental concepts and techniques in machine learning. It begins with a definition of machine learning and discusses both supervised and unsupervised learning methods, including Support Vector Machines and K-Means Clustering. Reinforcement learning is explored with its focus on maximizing reward functions, while genetic algorithms are described as optimization techniques. The presentation also highlights challenges, such as data requirements and potential drawbacks. This serves as a foundational resource for understanding how machines can learn from experience.

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Overview of Machine Learning

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Presentation Transcript

  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

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