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multi-dimensional data| 代写Network | network代做 | 代写Algorithm – 该题目是一个常规的multi-dimensional data的练习题目代写, 涉及了multi-dimensional data/Network/network/Algorithm等代写方面

Provost & Fawcett have defined Data Science in terms of 9 computational problems.

Define the Similarity problem in general and propose examples on multi-dimensional data.

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Spectral analysis can be used to reduce data dimensionality. Explain why dimensionality reduction is desirable and how Spectral analysis can achieve it.

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Over D = {a, b, c, d, e}, frequency of observations gives us the following distribution:

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P = Pr[X=xi] = [3/8, 3/16, 1/8, 1/8, 3/16].
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To simplify calculations, however, we decide to adopt the simpler distribution

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Q = Pr[X=xi] = [1/2, 1/8, 1/8, 1/8, 1/8].
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Compute the Kullback-Leibler divergence between P and Q, defined as

To simplify calculations, assume that log 2 3 (logarithm in base 2 of 3) equals 1.585 and show the process by which you calculated the divergence.

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Define the decision trees employed in the Supervised Segmentation task and describe in words how the CART Algorithm can recursively build a decision tree for a given dataset of labeled Yes/No examples.

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Sports Rating & Ranking: if a function S(i) measures the strength of a team/player i attending a tournament, how could we predict the outcome of a match between, say, team i and team j?

What method would you use, among those seen in class, to extract function S(i) from a dataset of past results?

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Define the Kernel method for creating a feature space and discuss why it is used in combination with Support Vector Machines to classify data.

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Define the Degree sequence of networks, explain why the sum of degrees is always even and discuss its usage in network analysis.

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Ranking in Networks: what is the model of i) importance and ii) human navigation of web pages that underpins PageRank?

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