Machine Learning
Find the Closest Pair of Points in 2D Space
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Given a set of points in 2D space, the goal is to find the closest pair of points. This can be done by brute force, which involves checking the distance between every pair of points and finding the minimum. However, this is computationally expensive. Therefore, another approach is to use a divide and conquer algorithm, which is more efficient.
Implementing a k-nearest neighbors algorithm.
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The k-nearest neighbors algorithm is a supervised learning algorithm that can be used to classify data points. Given a set of training data points and a test data point, the algorithm will output the class label of the test data point.
Design a machine learning algorithm for automated face detection in images.
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Design a machine learning algorithm that can automatically detect faces in images.
Design a machine learning algorithm to recognize handwritten digits with high...
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The challenge is to design a machine learning algorithm that can accurately recognize handwritten digits from a given data set.
Predicting Review Scores for New Products
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The goal is to design a machine learning model that can predict the review score for a new product, based on the prices and review scores of existing products.
Machine Learning Algorithm for Learning Binary Functions
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The goal is to design a machine learning algorithm that can learn any binary function from a set of input vectors and output values. The algorithm should be able to learn any function that is consistent with the training data.
Designing a Machine Learning Algorithm to Automatically Identify Animals in...
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Design a machine learning algorithm that can automatically identify different types of animals in images.
K-Nearest Neighbor Classifier
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K-Nearest Neighbor classifiers are used to classify data points into two classes. In this example, the input data points are (1, 1), (2, 2), (3 , 3), (4, 4), (5, 5), and (6, 6). The classifier should output Class 1: (1, 1), (2, 2), (3, 3), (4, 4), (5, 5) and Class 2: (6, 6).
