
Generative Models and Learning Quiz
Authored by 22022658 Khôi
Computers
University
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main difference between generative models and discriminative models?
Generative models only describe the conditional distribution of the output for a given input, while discriminative models describe the joint distribution of both inputs and outputs.
Generative models describe the joint distribution of both inputs and outputs, while discriminative models only describe the conditional distribution of the output for a given input.
Generative models have a deeper understanding of the data, while discriminative models can simulate synthetic data.
Generative models are designed to learn from data how to predict the output conditionally on a given input, while discriminative models are learned from data but have a wider scope.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of a generative model in machine learning?
To predict the output conditionally on a given input.
To learn from data how to predict the output conditionally on a given input.
To describe the joint distribution of both inputs and outputs.
To simulate synthetic data from the model.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the Gaussian Mixture Model (GMM) used for in supervised learning?
Training linear or quadratic discriminant analysis methods.
Solving the clustering problem.
Clustering unlabelled data points.
Learning from partially labelled data.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the key insight for using a generative model to make predictions?
To simulate data from the model.
To predict the output directly from the input.
To compute the conditional distribution of the output given the input.
To learn the joint distribution of the input and output.
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the Expectation-Maximisation (EM) algorithm in semi-supervised learning?
To discard unlabelled data points.
To predict missing output values for unlabelled data points.
To update the model parameters using labelled data only.
To learn the GMM from labelled data points only.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the key difference between the GMM model and k-means clustering?
GMM is a supervised learning method, while k-means is an unsupervised clustering method
GMM uses soft cluster assignments, while k-means uses hard cluster assignments
GMM is sensitive to input normalization, while k-means is not
GMM uses Euclidean distance, while k-means uses Mahalanobis distance
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of applying a clustering algorithm to the inputs in the music classification data Example 2.1?
To demonstrate the impact of input normalization on clustering results
To compare the performance of GMM and k-means algorithms
To illustrate the use of clustering without considering labels
To find the optimal number of clusters for the data
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