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Hill-Climbing algorithm terminates when, a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) Local Min/Max is achieved
3717One of the main cons of hill-climbing search is, a) Terminates at local optimum b) Terminates at global optimum c) Does not find optimum solution d) Fail to find a solution
1675Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. a) True b) False
1500Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next. a) Needy local search b) Heuristic local search c) Greedy local search d) Optimal local search
1 2935Hill-Climbing approach stuck for the following reasons a) Local maxima b) Ridges c) Plateaux d) All of above
3434___________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search
3320_________________ are mathematical problems defined as a set of objects whose state must satisfy a number of constraints or limitations. a) Constraints Satisfaction Problems b) Uninformed Search Problems c) Local Search Problems d) Only a) and b)
1685Which of the Following problems can be modeled as CSP? a) 8-Puzzle problem b) 8-Queen problem c) Map coloring problem d) Sudoku
2689What among the following constitutes to the incremental formulation of CSP? a) Path cost b) Goal cost c) Successor function d) Objective function e) Initial state
1787The term ___________ is used for a depth-first search that chooses values for one variable at a time and returns when a variable has no legal values left to assign. a) Forward search b) Backtrack search c) Hill algorithm d) Reverse-Down-Hill search
1 3182To overcome the need to backtrack in constraint satisfaction problem can be eliminated by a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals
1821To overcome the need to backtrack in constraint satisfaction problem can be eliminated by a) Forward Searching b) Constraint Propagation c) Backtrack after a forward search d) Omitting the constraints and focusing only on goals
1 2014The BACKTRACKING-SEARCH algorithm in Figure 5.3 has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is called chronological-backtracking. It is also possible to go all the way to set of variable that caused failure. State whether True or False. a) True b) False
1380Consider a problem of preparing a schedule for a class of student. This problem is a type of a) Search Problem b) Backtrack Problem c) CSP d) Planning Problem
3301Constraint satisfaction problems on finite domains are typically solved using a form of ___________. a) Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) DFS/BFS Search Algorithms
1762
What snow flake schema?
Why do we use robots in the industry?
What do you understand by normal distribution?
Consider the following algorithm: for ( i = 2 ; i <= n ; i++) { for ( j = 0 ; j <= n) { cout << i << j ; j = j + floor(n/4) ; } } (a) What is the output when n = 4 (b) What is the time complexity T(n). You may assume that n is divisible 4.
What is phonology natural language processing terminology?
Which search algorithm will use limited amount of memory? a) RBFS b) SMA* c) Hill-climbing search algorithm d) Both a & b
What is meant by binomial distribution?
Describe dimension reduction in machine learning.
Why do we need technology intelligence?
What is a sting?
Tell us which do you think is more important: model accuracy or model performance?
Explain the decision tree classification?
What is batch in machine learning?
What is the difference between type I and type ii error?
What is logistic and linear regression? How do you treat multicollinearity and heteroscedasticity in regression? Name some packages in r and python for building regression models.
