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Question 3 Please brainstorm and describe one machine learning task, either classification or regression, based on your own experiences and background. It should not be the demo tasks taught in class, e.g., hand digit recognition, house pricing, fish classification etc. Please design your machine learning algorithm and answer the following questions. Note that you only need to submit your plans. No source codes or results are required. Task. Please briefly explain the inputs, outputs, and your goal in general. Data preparation. Please describe how to collect dataset,

Question 3 Please brainstorm and describe one machine learning task, either classification or regression, based on your own experiences and background. It should not be the demo tasks taught in class, e.g., hand digit recognition, house pricing, fish classification etc. Please design your machine learning algorithm and answer the following questions. Note that you only need to submit your plans. No source codes or results are required. Task. Please briefly explain the inputs, outputs, and your goal in general. Data preparation. Please describe how to collect dataset,

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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ull T-Mobile LTE 3:04 PM 54% 4 < Вack ha1.docx 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 predictions; iii) unsupervised learning with discrete results; d. All the three scenarios can be solved by unsupervised learning. Question 3 Please brainstorm and describe one machine learning task, either classification or regression, based on your own experiences and background. It should not be the demo tasks taught in class, e.g., hand digit recognition, house pricing, fish classification etc. Please design your machine learning algorithm and answer the following questions. Note that you only need to submit your plans. No source codes or results are required. Task. Please briefly explain the inputs, outputs, and your goal in general. Data preparation. Please describe how to collect dataset, including training data, validation data, and testing data. Please explain how to get the ground-truth label for all samples. Submission instructions: what to hand in • Prepare Single PDF file to address the above questions. / Question 1: results and codes / Question 2: choices / Question 3: writings • Submit your PDF file and source codes using the CANVAS • No HARD COPY IS REQURIED. 3 2 Dashboard Calendar To Do Notifications Inbox
Transcribed Image Text:ull T-Mobile LTE 3:04 PM 54% 4 < Вack ha1.docx 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 predictions; iii) unsupervised learning with discrete results; d. All the three scenarios can be solved by unsupervised learning. Question 3 Please brainstorm and describe one machine learning task, either classification or regression, based on your own experiences and background. It should not be the demo tasks taught in class, e.g., hand digit recognition, house pricing, fish classification etc. Please design your machine learning algorithm and answer the following questions. Note that you only need to submit your plans. No source codes or results are required. Task. Please briefly explain the inputs, outputs, and your goal in general. Data preparation. Please describe how to collect dataset, including training data, validation data, and testing data. Please explain how to get the ground-truth label for all samples. Submission instructions: what to hand in • Prepare Single PDF file to address the above questions. / Question 1: results and codes / Question 2: choices / Question 3: writings • Submit your PDF file and source codes using the CANVAS • No HARD COPY IS REQURIED. 3 2 Dashboard Calendar To Do Notifications Inbox
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