Machine Learning (AD-502) - Important Questions

Explain the importance of continuous model monitoring and retraining in the machine learning life cycle.

Describe the stages of the machine learning life cycle from data collection to model deployment.

Define supervised learning and give examples of tasks solvable with it.

Briefly explain the types of machine learning.

Explain Artificial Intelligence, Machine Learning and Deep Learning in detail.

Define data science and list and explain different application areas of data science.

Describe hierarchical clustering and explain its methods including agglomerative hierarchical clustering and divisive DIANA method.

Explain distribution model-based clustering using Gaussian Mixture Models (GMMs) and the Expectation-Maximization (EM) algorithm.

Explain the concept of clustering in machine learning and how it differs from classification and regression.

Write a short note on any two of the following:

Discuss different application areas where Clustering is used?

Explain the concept of logistic regression in classification. How does it model the probability of class membership?

Describe decision tree classification and how a tree-based model is constructed for classification.

Discuss the advantages and disadvantages of decision trees for handling complex decision boundaries.

Is K-NN algorithm is supervised. Explain in detail.

Write a note on Classification Accuracy.

Explain the concept of ensemble learning in machine learning. What is the underlying idea behind ensemble methods?

Explain the averaging technique in ensemble learning and how it combines predictions from multiple models.

Describe the max voting technique in ensemble learning and how it works for classification.

What are bagging and pasting as ensemble techniques and what are the key differences between them?

Explain how the Random Forest algorithm produces output for regression problems.

Explain how a Random Forest is related to Decision Trees.

What are bagging and boosting and what are the differences between them?

Define principal components in the context of PCA. How are they calculated from the original data?

Differentiate between projection-based dimensionality reduction and manifold learning and explain their primary objectives.

Discuss the significance of the VC dimension in understanding the generalization ability of learning algorithms.

What is dimensionality reduction and what are the benefits of applying it?

Explain the Backward Elimination technique in detail.

List the main approaches for dimensionality reduction.