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Question 2 2.1 You are given the following tasks, all of which can be solved with a certain type of machine learning algorithms. (i) Estimate one's age given her/his facial photo. This regression would be based on some image features, e.g., pixel intensities, histogram of colors. The output is one's age (e.g., 55, 54.3 etc.) Classify an email to be spam or not. Users already identified some emails as spam (ii) ones. Human tumor Microarray data are provided as a matrix where rows correspond to genes and columns to tissue samples. The task is to cluster columns (or samples) to identify disease profiles: tissues with similar disease should yield similar expression profiles. (iii)

Question 2 2.1 You are given the following tasks, all of which can be solved with a certain type of machine learning algorithms. (i) Estimate one's age given her/his facial photo. This regression would be based on some image features, e.g., pixel intensities, histogram of colors. The output is one's age (e.g., 55, 54.3 etc.) Classify an email to be spam or not. Users already identified some emails as spam (ii) ones. Human tumor Microarray data are provided as a matrix where rows correspond to genes and columns to tissue samples. The task is to cluster columns (or samples) to identify disease profiles: tissues with similar disease should yield similar expression profiles. (iii)

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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Question 2 2.1 You are given the following tasks, all of which can be solved with a certain type of machine learning algorithms. Estimate one’s age given her/his facial photo. This regression would be based on some image features, e.g., pixel intensities, histogram of colors. The output is one's age (e.g., 55, 54.3 etc.) Classify an email to be spam or not. Users already identified some emails as spam (ii) ones. Human tumor Microarray data are provided as a matrix where rows correspond to genes and columns to tissue samples. The task is to cluster columns (or samples) to identify disease profiles: tissues with similar disease should yield similar expression profiles. (ii) Which statement is correct? (Mark one) a. i) unsupervised learning with discrete predictions; ii) supervised learning with continuous predictions; iii) supervised learning with continuous predictions; b. i) supervised learning with continuous predictions; ii) supervised learning with discrete predictions; iii) unsupervised learning with discrete results c. i) supervised learning with discrete predictions; ii) supervised learning with continuous I predictions; iii) unsupervised learning with discrete results; d. All the three scenarios can be solved by unsupervised learning. lish (US) W CL 000 F4 F3 F6 F7 F8 F9 F10 % & * 4 7 8
Transcribed Image Text:Question 2 2.1 You are given the following tasks, all of which can be solved with a certain type of machine learning algorithms. Estimate one’s age given her/his facial photo. This regression would be based on some image features, e.g., pixel intensities, histogram of colors. The output is one's age (e.g., 55, 54.3 etc.) Classify an email to be spam or not. Users already identified some emails as spam (ii) ones. Human tumor Microarray data are provided as a matrix where rows correspond to genes and columns to tissue samples. The task is to cluster columns (or samples) to identify disease profiles: tissues with similar disease should yield similar expression profiles. (ii) Which statement is correct? (Mark one) a. i) unsupervised learning with discrete predictions; ii) supervised learning with continuous predictions; iii) supervised learning with continuous predictions; b. i) supervised learning with continuous predictions; ii) supervised learning with discrete predictions; iii) unsupervised learning with discrete results c. i) supervised learning with discrete predictions; ii) supervised learning with continuous I predictions; iii) unsupervised learning with discrete results; d. All the three scenarios can be solved by unsupervised learning. lish (US) W CL 000 F4 F3 F6 F7 F8 F9 F10 % & * 4 7 8
2.0 1.5 - local max 1.0- 0.5 0.0- -0.5 -1.0 - -1.5 -2.0 1 2 3 5 Please select a proper range for the variable x, and visualize the following functions: Straight line y = 0, + 0,x where 0, = 30, 0, = 0.5 Quadratic function: y = (x – 6,)² + 0,,where 0, = 25, 0, = 20 Log function, y = – log(x) and y = – log(1 – x), Sigmoid function, y = 1/(1 + e-*) Ouestion 2 ish (US) CL F3 F4
Transcribed Image Text:2.0 1.5 - local max 1.0- 0.5 0.0- -0.5 -1.0 - -1.5 -2.0 1 2 3 5 Please select a proper range for the variable x, and visualize the following functions: Straight line y = 0, + 0,x where 0, = 30, 0, = 0.5 Quadratic function: y = (x – 6,)² + 0,,where 0, = 25, 0, = 20 Log function, y = – log(x) and y = – log(1 – x), Sigmoid function, y = 1/(1 + e-*) Ouestion 2 ish (US) CL F3 F4
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