Fundamentals of Machine Learning — Summer 2024

What is Bayesian Belief Network?

Write any four characteristics of a problem which is appropriate for decision tree.

lWeahranti ndgo. you mean by learning system? What is the role of (i) training experience and (ii) target function in designing a good learning system?

Define following terms w.r.t machine learning: (i) training set (ii) validation set (iii) testing set.

Give an example of a biased hypothesis space.

Explain MAP hypothesis and consistent learners with suitable examples.

Define: sensitivity, specificity. Make confusion matrix from given data and find sensitivity, specificity, accuracy and F-score from it: No. of samples in test data having class value POSITIVE = 100 No. of samples in test data having class value NEGATIVE = 300 No. of POSITIVE samples of test data classified as POSITIVE by model = 90 No. of NEGATIVE samples of test data classified as NEGATIVE by model = 280

What is multilayer feedforward artificial neural network?

Compare precision and recall. Find precision and recall for given data in confusion matrix: Predicted positive Predicted negative Actual positive 280 Actual negative 10 190

Differentiate supervised, unsupervised and semi-supervised machine learning paradigms with suitable examples. Clearly mention the applications of each one of them.

Define following terms with respect to machine learning:

consistent hypothesis (ii) underfitting (iii) mean square error

How does decision tree pruning help to avoid overfitting?

With any one recommender system, explain the mechanism of predictive model with its usefulness & limitations.

Write any three termination conditions in backpropagation algorithm.

Define following terms with respect to machine learning:

gradient descent (ii) learning rate (iii) cost function (iv) epoch

Explain maximum likelihood hypothesis for predicting probabilities.1

Define following terms with respect to machine learning:

target function (ii) inductive learning hypothesis (iii) version space

What is brute-force bayes concept learning?

What is post-pruning? Explain the reduced error pruning method with suitable example.

Differential between Gibbs algorithm and Bayes optimal classifier.

Briefly explain the steps to prepare the version space for given training dataset.

What is artificial neural network? For which type of the problems, learning by artificial neural network can be beneficial?

Define following terms with respect to machine learning:

precision (ii) overfitting (iii) concept learning

How does supervised machine learning help in text classification?

What is inductive bias in decision tree learning? Explain the role of Occam’s razor while considering the short hypothesis.