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

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Presentation transcript:

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

Unsupervised Learning ► Clustering  K-Means algorithm ► Reinforcement Learning  Q-learning algorithm

K-Means algorithm

Numerical Data Set K Input K-Means algorithm

K-Means algorithm Original Data (2 dimensions)

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 S t ► Choose action to perform A t ► Receive reward(reinforcement) R t = R(S t, A t ) ► Next State S t+1 = T(S t, A t )

Q-learning algorithm

► Try the Tower-of-Hanoi Game Tower-of-Hanoi GameTower-of-Hanoi Game

Reference ► Teknomo, Kardi. K-Means Clustering Tutorials. tutorial\kMean\