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2. We wish to classify four students' outcomes as pass or fail in a course during the online semester. We have 4 training samples as given in Table 1. Table 1: Perceptron data Attributes Student Outcome Attending tively (i) (ii) (iii) (iv) ac- Submitting assignments 1 Watching recordings -1 the 1 Pass -1 -1 -1 Fail -1 1 - 1 Fail 1 -1 -1 Pass Train a perceptron to fully classify them and show all steps until convergence. Choose the learning rate to be 1 and initial weights to be [0.25, 0.25, 0.25]. Analyze the final converged weight and what does it say? If you have got an attribute set of (-1, 1, 1], whether you will pass or fail?

2. We wish to classify four students' outcomes as pass or fail in a course during the online semester. We have 4 training samples as given in Table 1. Table 1: Perceptron data Attributes Student Outcome Attending tively (i) (ii) (iii) (iv) ac- Submitting assignments 1 Watching recordings -1 the 1 Pass -1 -1 -1 Fail -1 1 - 1 Fail 1 -1 -1 Pass Train a perceptron to fully classify them and show all steps until convergence. Choose the learning rate to be 1 and initial weights to be [0.25, 0.25, 0.25]. Analyze the final converged weight and what does it say? If you have got an attribute set of (-1, 1, 1], whether you will pass or fail?

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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2. We wish to classify four students' outcomes as pass or fail in a course during the online semester. We have 4 training samples as given in Table 1. Table 1: Perceptron data Attributes Student Outcome Attending tively (i) (ii) (iii) (iv) ac- Submitting assignments Watching recordings the 1 1 -1 Pass -1 -1 -1 Fail -1 1 - 1 Fail 1 -1 -1 Pass Train a perceptron to fully classify them and show all steps until convergence. Choose the learning rate to be 1 and initial weights to be [0.25, 0.25, 0.25]. Analyze the final converged weight and what does it say? If you have got an attribute set of (-1, 1, 1], whether you will pass or fail?
Transcribed Image Text:2. We wish to classify four students' outcomes as pass or fail in a course during the online semester. We have 4 training samples as given in Table 1. Table 1: Perceptron data Attributes Student Outcome Attending tively (i) (ii) (iii) (iv) ac- Submitting assignments Watching recordings the 1 1 -1 Pass -1 -1 -1 Fail -1 1 - 1 Fail 1 -1 -1 Pass Train a perceptron to fully classify them and show all steps until convergence. Choose the learning rate to be 1 and initial weights to be [0.25, 0.25, 0.25]. Analyze the final converged weight and what does it say? If you have got an attribute set of (-1, 1, 1], whether you will pass or fail?
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