CSDS 391 Intro to AI: Learning from Examples

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Questions and Answers

What is the primary focus of the course CSDS 391?

Operating Systems

Machine Learning Algorithms

Intro to AI (correct)

Data Structures

Learning from examples is a fundamental aspect of AI.

True

What type of data is focused on when predicting credit risk in AI?

credit risk data

In AI, learning from examples typically involves classifying __________ data.

uncertain Signup and view all the answers

Match the following AI concepts with their descriptions:

Learning from Examples = Utilizing data to improve predictions Classifying Uncertain Data = Sorting data into categories despite uncertainty Predicting Credit Risk = Assessing the likelihood of default on loans AI = Simulating human intelligence in machines Signup and view all the answers

What is a potential advantage of using distance metrics like Euclidean distance in clustering applications?

It does not require an explicit model. Signup and view all the answers

Euclidean distance is an effective method for handling high-dimensional data classification.

True Signup and view all the answers

What is the classification error percentage for the 7-nearest neighbor approach on handwritten digits using leave-one-out?

4.85% Signup and view all the answers

The distance metric formula $d(x, y) = \sum (x_i − y_i)^2$ is used to calculate __________.

Euclidean distance Signup and view all the answers

Match the following terms with their descriptions:

K-means = A clustering algorithm that partitions data into k clusters. Leave-one-out = A method for validating the performance of a model. Euclidean distance = A distance metric calculated by the square root of summing squared differences. 7-nearest neighbor = A classification approach that uses the closest 7 examples for decision-making. Signup and view all the answers

Which of the following is NOT a characteristic of clustering techniques?

Clustering requires prior knowledge of class labels. Signup and view all the answers

A disadvantage of using Euclidean distance is that it may not perform well in all cases.

True Signup and view all the answers

Why is it said that using distance metrics requires no 'brain' on the part of the designer?

Because it automates the classification process based on data features. Signup and view all the answers

Which of the following describes the k-nearest neighbors algorithm?

It looks at k-nearest neighbors and chooses the most frequent class. Signup and view all the answers

The Euclidean distance metric considers how many pixels overlap in image data.

True Signup and view all the answers

What kind of distance metric could be defined to improve classification in k-nearest neighbors?

A distance metric that is insensitive to small deviations in position, scale, or rotation. Signup and view all the answers

The k-nearest neighbors algorithm is typically evaluated based on its error rate on __________ data.

test Signup and view all the answers

What potential issue arises when finding neighbors in the k-nearest neighbors algorithm?

It can become computationally expensive. Signup and view all the answers

Match the following clustering techniques with their characteristics:

K-means = Partitions data into k clusters based on distance to the centroid. Hierarchical clustering = Creates a tree-like structure of clusters. DBSCAN = Clusters based on density of data points. Mean-shift = Finds modes of the data distribution. Signup and view all the answers

Small deviations in image position, scale, or rotation can significantly impact Euclidean distance calculations.

True Signup and view all the answers

What is the main objective of using distance metrics in clustering algorithms?

To accurately measure the similarity or dissimilarity between data points. Signup and view all the answers

What is the primary criterion for the optimal decision boundary in classification?

Minimizing the misclassification error Signup and view all the answers

Euclidean distance is commonly used to measure the similarity between points in nearest neighbor classification.

True Signup and view all the answers

What is the assumption made about class probability in the context of the optimal decision boundary?

p(C3 |x) = p(C2 |x) Signup and view all the answers

In classification, points that are nearby are likely to belong to the same __________.

class Signup and view all the answers

Match the following distance metrics with their applications:

Euclidean distance = Measuring straight-line distance in multi-dimensional space Manhattan distance = Calculating distance along axes at right angles Minkowski distance = Generalization of Euclidean and Manhattan distances Cosine similarity = Determining the angle between two vectors Signup and view all the answers

What does 'p(x|C2)' refer to in a classification context?

Probability of feature x given class C2 Signup and view all the answers

A misclassification error occurs when a classified point is correctly categorized into its true class.

False Signup and view all the answers

Name one clustering technique other than K-means.

Hierarchical clustering Signup and view all the answers

Study Notes