Machine learning Programming and Concepts With Interview Questions | Viva Questions
Machine learning Programming and Concepts based on vtu syllabas (15CSL76)
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| Machine learning program by aryadrj |
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1.Implement and demonstrate FIND-S algorithm for finding the most specific hypothesis based on given set of training data samples. read the training data from .CSV file.
->Click here to open program <-
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2.For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithmto output a description of the set of all hypotheses consistent with the training examples.
->Click here to open program <-
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3.Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge toclassify a new sample.
->Click here to open program-<
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4.Build an Artificial Neural Network by implementing the Back propagation algorithm and test the same using appropriate data sets.
->Click here to open program<-
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5.Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.
->Click here to open program<-
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6.Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data set.
->Click here to open program<-
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7.Write a program to construct aBayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Java/Python ML library classes/API.
->Click here to open program<-
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8.Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.
->Click here to open program<-
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10.Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.
->Click here to open program <-
============================
2.For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithmto output a description of the set of all hypotheses consistent with the training examples.
->Click here to open program <-
============================
3.Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge toclassify a new sample.
->Click here to open program-<
============================
4.Build an Artificial Neural Network by implementing the Back propagation algorithm and test the same using appropriate data sets.
->Click here to open program<-
============================
5.Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.
->Click here to open program<-
=============== ============
6.Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data set.
->Click here to open program<-
===========================
7.Write a program to construct aBayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Java/Python ML library classes/API.
->Click here to open program<-
===========================
8.Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.
->Click here to open program<-
===========================
9.Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem.
===========================10.Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.
1. What is machine learning?
2. Define supervised learning
3. Define unsupervised learning
4. Define semi-supervised learning
5. Define reinforcement learning
6. What do you mean by hypotheses
7. What is classification
8. What is clustering
9. Define precision, accuracy, and recall
10.Define entropy
11.Define regression
12.How KNN is different from k-means clustering
13. What is concept learning
14.Define specific boundary and general boundary
15.Define the target function
16.Define the decision tree
17. What is ANN
18.Explain gradient descent approximation
19.State Bayes theorem
20.Define Bayesian belief networks
21.Differentiate hard and soft clustering
22.Define variance
23. What is inductive machine learning
24.Why K nearest neighbor algorithm is lazy learning algorithm
25.Why naïve Bayes is naïve
26.Mention classification algorithms
27.Define pruning
28.Differentiate Clustering and classification
29.Mention clustering algorithms
30.Define Bias
====================2. Define supervised learning
3. Define unsupervised learning
4. Define semi-supervised learning
5. Define reinforcement learning
6. What do you mean by hypotheses
7. What is classification
8. What is clustering
9. Define precision, accuracy, and recall
10.Define entropy
11.Define regression
12.How KNN is different from k-means clustering
13. What is concept learning
14.Define specific boundary and general boundary
15.Define the target function
16.Define the decision tree
17. What is ANN
18.Explain gradient descent approximation
19.State Bayes theorem
20.Define Bayesian belief networks
21.Differentiate hard and soft clustering
22.Define variance
23. What is inductive machine learning
24.Why K nearest neighbor algorithm is lazy learning algorithm
25.Why naïve Bayes is naïve
26.Mention classification algorithms
27.Define pruning
28.Differentiate Clustering and classification
29.Mention clustering algorithms
30.Define Bias
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