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Lecture 15 - K-Means Clustering

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Computer Science - Artificial Intelligence

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mmoranwkf Created by 9 mon ago

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Cards in this deck(18)
What is the primary goal of unsupervised learning algorithms, and how does it differ from supervised learning?
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What are the key tasks of unsupervised learning? Select all that apply.
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What are the key utilities of unsupervised learning in data analysis?
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What are the three main classes or types of unsupervised learning, and what do they involve?
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What are the applications and assumptions associated with clustering in data analysis?
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What is K-means clustering, and what assumptions are associated with it?
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What are the steps involved in the K-means clustering algorithm?
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What is the objective of the K-means algorithm, and how is it mathematically represented?
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How do you optimize the K-means objective function?
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Mathematically describe the K-means steps. What do the assignment and update steps look like?
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What are the model parameters involved in K-means clustering?
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Describe the process of using K-means for image segmentation to identify perceptually meaningful groups of pixels.
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What happens if K is set to a small value when segmenting an image?
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Does the K-means objective function J always find the best solution possible?
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How do you address getting stuck in a local minima with K-means?
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How do you determine the optimal value of K in K-means clustering?
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What is soft K-means, and what are its benefits in clustering?
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Mathematically describe the soft K-means steps. What do the assignment and update steps look like?
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