Machine Learning (IT-802 (A)) - Important Questions

Explain the differences between supervised, unsupervised, semi-supervised and reinforcement learning with practical examples for each.

What are the key factors affecting the performance of a machine learning model? Discuss their significance with examples.

Compare finite and infinite hypothesis spaces in machine learning. What are the implications of each on model complexity and generalization?

Explain the various learning paradigms with examples, and finite vs infinite hypothesis spaces.

Describe the key concepts of Support Vector Machines for linear classification and how the margin is optimized.

Explain the role of activation functions in a multilayer perceptron. How do they enable the learning of nonlinear patterns?

Differentiate between linear regression, multiple linear regression, and logistic regression. Provide examples where each would be appropriate.

Explain the ID3 algorithm for building decision trees. How does it use entropy and information gain?

Solve linear regression for given data, assess goodness of fit, and predict sales for a given time value.

Classify linearly separable data using SVM and find the support vectors and maximum margin hyperplane.

Apply appropriate classification algorithm on below data table and classify the class of product with Previous Price=42 and New Price=48.

Name Previous Price New Price Class Product 1 43 54 Sell Product 2 52 61 Sell Product 3 38 40 Accumulate Product 4 29 21 Accumulate

Explain Linear and Logistic regression. What are the advantages for the same?

Explain the objective of convergence of the cost function, its use in linear regression, and its relation to SSE.

Discuss the role of meta-learners in stacking and how they contribute to the overall model performance.

What is the bagging technique in ensemble learning, and how does it reduce variance in predictions?

Compare and contrast the different model combination schemes used in ensemble learning.

Explain hierarchical clustering and compare AGNES and DIANA methods, including their advantages and limitations.

Compare PCA and Locally Linear Embedding (LLE). When would you prefer LLE over PCA?

Describe Gaussian Mixture Models (GMM) and how they model data clusters as probabilistic distributions. What makes GMMs suitable for overlapping clusters?

Suppose that the data mining task is to cluster the following eight points (with $(x, y)$ representing location) into three clusters: $A_{1}(2, 10), A_{2}(2, 5), A_{3}(8, 4), B_{1}(5, 8), B_{2}(7, 5), B_{3}(6, 4), C_{1}(1, 2), C_{2}(4, 9)$. The distance function is Euclidean distance. Suppose initially we assign $A_{1}, B_{1}$, and $C_{1}$ as the center of each cluster, respectively. Use the k-means algorithm to show only:

Assuming the database given in the table below. Find the distance using Euclidean distance method with respect to Cluster Center (0.30, 0.20).

Items X Y C1 0.40 0.53 C2 0.22 0.38 C3 0.35 0.32 C4 0.26 0.19 C5 0.08 0.41 C6 0.45 0.30

Explain and elaborate Self-Organizing Map. Justify your answer with neat diagram and example.

What is Principal Component Analysis (PCA)? Explain using an example. Also explain the concept of Dimensionality reduction.

What is frequent pattern mining and why is it important in market basket analysis?

Explain the process of constructing a Bayesian Belief Network, including defining nodes, edges and conditional probability tables.

State the assumptions of the Naive Bayes classifier and explain how they simplify probability computation.

Define prior and posterior probability in Naive Bayes and explain with an example how noise affects classifier performance.

Explain Bayesian learning in probabilistic learning with a suitable example.

State the k-frequent itemset mining algorithm and solve a problem using k=3 with minimum support 2.

Apply Bayes' theorem to compute posterior probability of drug use given a positive test with given false positive, false negative and prior rates.

Given a Bayesian network with prior and conditional probabilities, construct the joint probability table.