CS4378V_CS5369L: Homework #2 CS4378V_CS5369L: Homework #2 Assigned: Tuesday, October 9, 2012 Due: Tuesday, October 23, 2012 (100 points)CS4378V_CS5369L: Homework #2 1. (40 pts) Implement the linear regression algorithm (gradient descent) and run the linear regression algorithm on the training samples (linear_regression_data.txt). You can find the dataset on Tracs under â€œResources: Dataâ€. Get the learned linear function and the predicted results of the testing samples (linear_regression_data.txt). 2. (40 pts.) Learn the probabilities for Naive Bayes for the following training examples. Use Laplaceâ€™s smoothing to estimate conditional probabilities. How well does your Naive Bayes hypothesis perform on the following test examples? Show your work. Attributes Class x1 x2 x3 x4 y 0 0 1 0 1 0 1 0 1 1 1 0 0 0 -1 1 0 1 1 -1 3. (20 pts) Design an n-input perceptron that implements the function: if k or more of the inputs are true, the output is true. (Suppose the input value can only be â€œ1â€ (true) or â€œ0â€ (false))
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