What is a class diagram in UML, and what does it represent? It illustrates the user interface layout. It represents the flow of data in a system. It depicts the sequence of operations in a process. It shows the classes of a system and the associations between those classes.

It shows the classes of a system and the associations between those classes.

What are the main symbols shown on class diagrams in UML? Nodes, Edges, Labels, Weights Actors, Use Cases, System Boundaries, Relationships Entities, Relationships, Attributes, Keys Classes, Associations, Attributes, Operations, Generalizations

Classes, Associations, Attributes, Operations, Generalizations

What does multiplicity define in the context of UML class diagrams? It defines how many instances can be associated at any given moment. It indicates the inheritance hierarchy. It determines the visibility of a class. It specifies the data type of an attribute.

It defines how many instances can be associated at any given moment.

What is aggregation in UML class diagrams? A strong kind of aggregation Special associations representing 'part-whole' relationships A type of inheritance A method of encapsulation

Special associations representing 'part-whole' relationships

What is an interface in the context of UML and object-oriented design? A type of inheritance hierarchy A class with instance variables and implemented methods A description of a portion of the visible behavior of a set of objects A method of data encapsulation

A description of a portion of the visible behavior of a set of objects

What is the primary goal of unsupervised learning algorithms, and how does it differ from supervised learning? To find patterns without explicit feedback; unlike supervised learning, which uses labeled data. To minimize loss using labeled data, similar to supervised learning. To classify data into predefined categories. To predict future outcomes based on historical data.

To find patterns without explicit feedback; unlike supervised learning, which uses labeled data.

What are the key tasks of unsupervised learning? Select all that apply. Predict future trends, classify data, optimize algorithms. Segment data, enhance image quality, increase resolution. Find clusters, model data density, find hidden causes, reduce dimensionality. Minimize loss, use labeled data, improve accuracy.

Find clusters, model data density, find hidden causes, reduce dimensionality.

What are the key utilities of unsupervised learning in data analysis? Predict outcomes, classify data, enhance accuracy. Minimize errors, use labeled data, improve precision. Segment images, increase resolution, enhance quality. Compress data, detect outliers, facilitate other learning.

Compress data, detect outliers, facilitate other learning.

What are the three main classes or types of unsupervised learning, and what do they involve? Data mining, data cleaning, data transformation. Clustering, density estimation, dimensionality reduction. Supervised learning, reinforcement learning, semi-supervised learning. Regression, classification, prediction.

Clustering, density estimation, dimensionality reduction.

What are the applications and assumptions associated with clustering in data analysis? Error minimization, precision enhancement, accuracy increase; assumes consistent data. Data classification, trend prediction, accuracy improvement; assumes labeled data. Image enhancement, resolution increase, quality improvement; assumes uniform data distribution. Prediction, outlier detection, lossy compression, data segmentation; assumes data from different classes.

Prediction, outlier detection, lossy compression, data segmentation; assumes data from different classes.

What is K-means clustering, and what assumptions are associated with it? Categorizes data into K classes assuming Euclidean space. Predicts future trends using historical data. Classifies data into predefined categories. Enhances image quality by increasing resolution.

Categorizes data into K classes assuming Euclidean space.

What are the steps involved in the K-means clustering algorithm? Predict outcomes, classify data, optimize algorithms. Minimize errors, use labeled data, improve precision. Initialize centroids, assign data points, update centroids, repeat until convergence. Segment images, increase resolution, enhance quality.

Initialize centroids, assign data points, update centroids, repeat until convergence.

What is the objective of the K-means algorithm, and how is it mathematically represented? Enhance the quality of image segmentation. Increase the resolution of data points. Maximize the accuracy of predictions. Minimize distances of data points to their cluster centroids.

Minimize distances of data points to their cluster centroids.

How do you optimize the K-means objective function? Increase the resolution of data points using stochastic gradient descent. Maximize the accuracy of predictions using gradient descent. Enhance the quality of image segmentation using backpropagation. Minimize each variable one at a time using block coordinate descent.

Minimize each variable one at a time using block coordinate descent.

Mathematically describe the K-means steps. What do the assignment and update steps look like? Assign data points to nearest mean using arg min, update means using sample means. Predict outcomes using regression, classify data using decision trees. Minimize errors using loss functions, improve precision using optimization algorithms. Enhance image quality using convolutional layers, increase resolution using pooling layers.

Assign data points to nearest mean using arg min, update means using sample means.

Who developed sophisticated programmable mechanical computers, precursors to modern electronic computers? Alan Turing and John von Neumann Isaac Newton and Galileo Galilei Charles Babbage and Lady Lovelace Pascal and Leibniz

Charles Babbage and Lady Lovelace

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