Machine Learning (AD-502) - Important Questions

Explain the different types of machine learning systems: supervised and unsupervised learning, batch and online learning, and instance-based versus model-based learning with suitable examples.

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.

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

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 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.

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

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.

Explain how a Random Forest is related to Decision Trees.

List the main approaches for dimensionality reduction and explain projection and manifold learning with their objectives.

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

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

Discuss different application areas where clustering is used?