Midterm Exam

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Arizona State University *

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589

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Electrical Engineering

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May 18, 2024

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pdf

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EEE 589: Machine Learning Course Midterm Exam Instructions Duration: 2 hours Answer all questions. Show all work for full credit. Use the provided equation sheet as needed. Questions 1. (10 points) Define machine learning. Describe the difference between supervised and unsupervised learning with examples. 2. (10 points) Explain the concept of overfitting and underfitting in machine learning. How can they be prevented? 3. (10 points) Consider a dataset with features X = [ x 1 , x 2 , x 3 , . . . , x n ] and target variable y . Write the equation for a simple linear regression model. Describe the method used to determine the parameters of this model. 4. (15 points) Given the following data points: (1 , 2), (2 , 3), (3 , 4), (4 , 5), fit a linear regression model and find the slope and intercept. 5. (15 points) The following table shows the confusion matrix for a binary classification problem. Calculate the accuracy, precision, recall, and F1- score. Predicted Positive Predicted Negative Actual Positive 50 10 Actual Negative 5 35 6. (10 points) Describe the purpose of feature scaling. Compare and con- trast two methods for feature scaling. 1
7. (10 points) Explain the concept of regularization in machine learning. Compare L1 (Lasso) and L2 (Ridge) regularization. 8. (10 points) Describe the k-Nearest Neighbors (k-NN) algorithm. What are its advantages and disadvantages? 9. (10 points) Define and explain the term ”dimensionality reduction”. De- scribe Principal Component Analysis (PCA) and its applications. 10. (10 points) Discuss the challenges of working with imbalanced datasets. What techniques can be used to address these challenges? 2
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