MS-DS Master of Data science Master of Data science Unsupervised Learning Techniques 1

Question 1

Which unsupervised learning algorithm assigns each data point to the nearest cluster centroid and iteratively updates centroids until convergence?

Accepted Answer

K-Means

Answer

K-Means iteratively assigns points to the nearest of K centroids (by Euclidean distance) and recomputes each centroid as the mean of its assigned points, repeating until assignments stop changing.

Question 2

In a Gaussian Mixture Model (GMM), the Expectation-Maximization (EM) algorithm alternates between which two steps?

Accepted Answer

Computing posterior probabilities and updating distribution parameters

Answer

The E-step computes the posterior probability (responsibility) that each Gaussian component generated each data point; the M-step updates the means, covariances, and mixture weights to maximize the expected log-likelihood.

Question 3

What is the primary purpose of t-SNE (t-Distributed Stochastic Neighbor Embedding)?

Accepted Answer

Visualizing high-dimensional data in two or three dimensions while preserving local structure

Answer

t-SNE converts pairwise similarities in high-dimensional space to probabilities and minimizes the KL divergence between those probabilities and ones in a low-dimensional embedding, making it ideal for 2-D or 3-D visualization of complex datasets.

Question 4

In hierarchical agglomerative clustering, which linkage criterion defines the distance between two clusters as the maximum pairwise distance between their members?

Accepted Answer

Complete linkage

Answer

Complete linkage uses the farthest (maximum) distance between any point in one cluster and any point in the other, producing compact, roughly equal-sized clusters and reducing the chaining effect seen with single linkage.

Question 5

An autoencoder trained without labels learns a compressed representation of input data in its bottleneck layer. What is this bottleneck representation called?

Accepted Answer

Latent code (or latent space)

Answer

The encoder compresses input into a lower-dimensional latent code; the decoder attempts to reconstruct the original input from it. The latent code captures the most salient features without any labeled supervision.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Master of Data science Unsupervised Learning Techniques 1