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CSC411- Machine Learning and Data Mining Unsupervised Learning

University of Toronto (Mississauga Campus). CSC411- Machine Learning and Data Mining Unsupervised Learning. Tutorial 9– March 16 th , 2007. Unsupervised Learning. Clustering K-Means algorithm Reinforcement Learning Q-learning algorithm. K-Means algorithm. K-Means algorithm. K. Input.

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CSC411- Machine Learning and Data Mining Unsupervised Learning

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  1. University of Toronto (Mississauga Campus) CSC411- Machine Learning and Data MiningUnsupervised Learning Tutorial 9– March 16th, 2007

  2. Unsupervised Learning • Clustering • K-Means algorithm • Reinforcement Learning • Q-learning algorithm

  3. K-Means algorithm

  4. K-Means algorithm K Input Numerical Data Set Numerical Data Set

  5. K-Means algorithm Original Data (2 dimensions)

  6. Reinforcement Learning • Markov Decision Processes (MDP) • MDP(S, A, T, R) • S: environment states • A: actions available to the agent • T: state transition function • R: reward function • At each step t: • Observe current state St • Choose action to perform At • Receive reward(reinforcement) Rt= R(St, At) • Next State St+1 = T(St, At)

  7. Q-learning algorithm

  8. Q-learning algorithm

  9. Try the Tower-of-Hanoi Game

  10. Reference • Teknomo, Kardi. K-Means Clustering Tutorials. http:\\people.revoledu.com\kardi\ tutorial\kMean\

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