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Exercise 1 Consider the following two datasets. Dataset 1 Dataset 2 0.0 -1.0 1.3 2.0 3.4 2.4 5.0 2.8 7.4 9.1 where each entry stands for a feature. You are required to calculate the mean and variance for each dataset and discuss which dataset consists of data points spreading out much more.

Exercise 1 Consider the following two datasets. Dataset 1 Dataset 2 0.0 -1.0 1.3 2.0 3.4 2.4 5.0 2.8 7.4 9.1 where each entry stands for a feature. You are required to calculate the mean and variance for each dataset and discuss which dataset consists of data points spreading out much more.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Exercise 1 Consider the following two datasets. Dataset 1 Dataset 2 0.0 -1.0 1.3 2.0 3.4 2.4 5.0 2.8 7.4 9.1 where each entry stands for a feature. You are required to calculate the mean and variance for each dataset and discuss which dataset consists of data points spreading out much more. Exercise 2 The algorithm to produce several principal components (shown at the last page of the slide) depends on the update of dataset X to X' as follows: x' := {1 – zAA" | € X} where X is an M x N-matrix for the number M of data points and the number N of features, and A is a parameter (weight) column vector of the size N for computing principal components on x. Confirm A is no informative on X' by proving a'A equivalent to 0 for each z' e X'.
Transcribed Image Text:Exercise 1 Consider the following two datasets. Dataset 1 Dataset 2 0.0 -1.0 1.3 2.0 3.4 2.4 5.0 2.8 7.4 9.1 where each entry stands for a feature. You are required to calculate the mean and variance for each dataset and discuss which dataset consists of data points spreading out much more. Exercise 2 The algorithm to produce several principal components (shown at the last page of the slide) depends on the update of dataset X to X' as follows: x' := {1 – zAA" | € X} where X is an M x N-matrix for the number M of data points and the number N of features, and A is a parameter (weight) column vector of the size N for computing principal components on x. Confirm A is no informative on X' by proving a'A equivalent to 0 for each z' e X'.
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