Machine Learning
Design a binary classification learning algorithm.
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The goal is to design a binary classification learning algorithm that can accurately predict the labels of test data, given a set of training data. Each data point in the training set consists of a set of features x_i \in \R^d and a label y_i \in \{-1, 1\}.
Web Page Classification System
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A web page classification system that automatically categorizes web pages into predefined categories is needed. Categories could be sports, news, entertainment, technology, etc.
Find the Line of Best Fit
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This technical problem involves finding the line of best fit for a set of data points. In the example given, the output would be a line with a slope of 2.
Implementing a k-nearest neighbors algorithm for classification.
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This technical problem deals with implementing a k-nearest neighbors algorithm for classification. Given a set of data points with labels, the goal is to determine the k-nearest neighbors of each data point.
Design a machine learning algorithm to learn a function that can map input...
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The goal is to design a machine learning algorithm that can learn a function mapping input vectors to output vectors, from a training set of input-output vector pairs. The function should be able to generalize to unseen inputs.
Machine Learning Algorithm for Automatic Blog Post Categorization
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The goal is to design a machine learning algorithm that can automatically categorize blog posts into one of several predefined categories. This can be done by training the algorithm on a dataset of labeled blog posts, where each post is already categorized into one of the predefined categories. Once the algorithm is trained, it can then be used to categorize new, unlabeled blog posts.
"Largest sum of non-adjacent numbers"
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This technical problem deals with finding the largest sum of non-adjacent numbers in a given list of integers. The input is a list of integers, and the output is the largest sum of non-adjacent numbers.
Implementing a k-means clustering algorithm.
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This technical problem deals with implementing a k-means clustering algorithm. The input is a set of data points in d-dimensional space, and the output is a partition of those data points into k clusters.
