multi-dimensional data | 代写Network | network代做 | 代写Algorithm – multi-dimensional data


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

network代做 代写network 计算机网络

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.

Your answer:

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

Your answer:

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

P = Pr[X=xi] = [3/8, 3/16, 1/8, 1/8, 3/16].

To simplify calculations, however, we decide to adopt the simpler distribution

Q = Pr[X=xi] = [1/2, 1/8, 1/8, 1/8, 1/8].

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.

Your answer:

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.

Your answer:

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?

Your answer:

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

Your answer:

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

Your answer:

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

Your answer: