Find the solution set of the following Activity-selection Problem. • By earliest starting time: sorting starting time (s;) in non-decreasing order. ⚫ By earliest finish time: sorting finishing time (fi) in non-decreasing order. By shortest interval: sorting (fi-Si) time in non-decreasing order. ai 1 2 3 4 5 6 7 8 9 10 11 Si 3 1 0 12 5 8 3 2 8 6 50 fi 5 10 4 6 14 9 11 8 13 12 10 7
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- esc A Question 14 of 20: Select the best answer for the question. 14. Use Gauss-Jordan elimination to solve the following linear system: -3x + 4y = -6 5x - y = 10 O A. (2,0) O B. (2,-5) OC. (-6,2) O D. (2,2) O Mark for review (Will be highlighted on the review page) > ← 21 W = S #3 E D لم G $ 4 { R بر 19 F % 50 F ف Oll GY O U *00 C 8 ASuppose a salesperson is planning a sales trip that includes n cities. Each city is connected to some of the other cities by a road. To minimize travel time, the salesperson wants to determine the shortest route that starts at the salesperson’s home city, visits each of the cities once, and ends up at the home city. This problem of finding the shortest route is called the Travelling Salesperson Problem (TSP) and is a well-known problem that can be solved using Dynamic Programming. Research about the TSP problem and find an algorithm based on Dynamic Programming for that. Explain the approach, the algorithm, and its time complexity. Use an example to explain the way the algorithm operates1. Let S = {1,2...n} be a set activities, where activity i € S has a start time si, a finish time f; and a profit p, asSociated with it. Design an dynamic programming algorithm for computing a set of compatible activities of maximum profit. Argue that the time complexity of you algorithm is O(n log(n)). (You must start with the correct definitions, and then write a recurrence relation.)
- If you can make an optimal solution for a problem by making optimal solutions for its subproblems, then the problem has the quality in question. a) Subproblems that overlap; b) best substructure; c) memory; d) greedy: |: Section 3 - Random Forests Decision Trees on their own are effectiev classifiers. The true power, though, comes from a forest of trees -- multiple decision trees working together as an ensemble learner. We create multiple trees, each using a subset of the available attributes, let them each make a guess at the correct classification, and then take the majority vote as the predicted class. Sounds tricky, right? Once again, sklearn to the rescue. We are going to see if a random forest can improve on our decision tree accuracy for the bank data. To begin, we are going to create a Random Forest using the wheat training and test data from the previous section. First, reload our data. 1 # reload the wheat dataset from UCI 2 3 df = pd. read_csv ("seeds_dataset.txt", sep='\\t', engine='python') 4 6 7 df.columns = ['a', 'p', 'compactness', 'length', 'width', 'coeff', 'length_g', 'type'] print (f'Our data has {df.shape [0]} rows and {df.shape [1]} columns') 8 #Mark 70% of the data for…pleaseee solve question 2 please
- Knapsack 0/1 problem: Given N items where each item has some weight and profit associated with it and also given a bag with capacity W, [i.e., the bag can hold at most W weight in it]. The task is to put the items into the bag such that the sum of profits associated with them is the maximum possible. Given the problem is solved using a dynamic programming approach and the matrix derived is given below, answer the below set of questions by analyzing the DP matrix. weights = [2, 3, 4, 5], profits = [1, 2, 5, 6], Capacity W = 8 Capacity 2 3 Profits weights|0 1 2 5 16 14 |-> 5 10 0 O 10 1 2 3 4 0 O 0 1 1 0 1 0 1 O 10 1 2 2 2 5 2 O 15 50 1 356 6 O 1 3 6 18 00378 7 10 10 1 3 7 7 1 18Q1 Dan has a list of problems suitable for the assignment. The difficulties of these problems are stored in a list of integers a. The i-th problem’s difficulty is represented by a[i] (the higher the integer, the more difficult the problem). Dan is too busy eating saltines to worry about assignment decisions, so he asks Michael to select at least two problems from the list for the assignment. Since there are many possible subsets of the problems to consider and Michael has a life, he decides to consider only sublists (definition follows) of the list of problems. To make grading the assignment easier, Michael wants to pick problems that don’t vary too much in difficulty. What is the smallest difference between the difficulties of the most difficult selected problem and the least difficult selected problem he can achieve by selecting a sublist of length at least 2 of the original list of problems? Definition: A sublist of a list a is any list you can obtain by removing some (possibly 0)…Machine Learning Problem Perform the optimization problem of finding the minimum of J(x) = (2x-3)2 by: (i) defining theta, J(theta), h(theta) as defined in the Stanford Machine Learning videos in Coursera; (ii) plotting J(theta) vs theta by hand then use a program (iii) determining its minimum using gradient descent approach starting from a random initial value of theta = 5. Perform the search for the minimum using the gradient descent approach by hand calculations, i.e., step 1, step 2, etc. showing your work completely