Reference no: EM133397784
Algorithms for Data Science
Question 1: Class, the below is a standard set of instructions for each HW, in this assignment groups will be set up for collaboration.",
"Make sure your group starts one thread for the collaborative problems. You are required to participate in the collaborative problem and subproblem separately. Please do not directly post a complete",
"solution, the goal is for the group to develop a solution after everyone has participated. Please ensure",
"you have a write-up with solutions to each problem and subproblems, you are also required to submit",
"code that will be compiled when grading the assignment. In each of the problems you are allowed to",
"use built-in functions.
Module 13 Note this is Collaborative Problem - Note: create threads for both subparts, you are required to participate in both subparts.__",
"*30 Points Total*",
"In this problem you will use a built-in Convolutional Neural Network (CNN) using either the Iris or numerical (MNIST) data sets and show the classification accuracy:"
In this problem, you will develop the Support Vector Machine (SVM) algorithm from scratch to classify the Iris data set."
Using the SVM in the Optimization course notes, develop psuedocode for an SVM classifier using a linear and separately an rbf kernel.",
Question 2. no need to discuss collaboratively - Analyze the runtime of your design in big O notation and calculate a total runtime such that each line of psuedocode is accounted for.,
Question 3. Implement your SVM using Python:,
- Train three two class models using the Iris dataset as input training data, the Iris data will need to be reconfigured as a one vs. all or one vs. one data set.
- Process the test data set to determine which class each test observation belongs to, in this problem you will simply use all 150 observations as your test data.",
" - What is the classification accuracy of your design?",
" - Is there a difference in performance between the two kernels? Why do you think that is?"
Attachment:- Algorithms for Data Science.rar
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