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Review Final Exam

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    It is about CS331
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  • In PCA, what does the first principal component maximize?
    Variance
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  • K-means is what type of learning?
    Unsupervised
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  • What is the main goal of PCA?
    PCA reduces dimensions while preserving maximum variance.
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  • What does K represent in K-means?
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  • If K increases, WCSS(cost function) usually:
    Decreases
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  • What happens first in K-means?
    Assign points
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  • MDP stands for:
    Markov Decision Process
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  • Which are components of an MDP?
    States, Actions, Rewards
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  • What does transition function T(s,a,s') represent?
    Probability of moving to state s′ after action a in state s.
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  • What does γ (gamma) represent?
    Discount factor
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  • If γ = 0, the agent cares about:
    Immediate reward only
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  • Policy iteration consists of:
    Policy evaluation + policy improvement
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  • If Q(s,a) values are: Left = 3 Right = 7 Up = 5 What action will policy choose?
    Right
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  • How is a centroid updated in K-means?
    By taking the mean of all assigned points.
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  • Why can K-means give different results on the same dataset?
    Random centroid initialization.
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  • What is a terminal state?
    A state where the episode ends.
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