# Machine Learning | Sample Assignment | Assignment Help

1. Consider a perceptron learning algorithm (PLA) used for a binary classification problem

(adaptive decision boundary).

**Question: **

What will be the algorithm’s output if the training data are not linearly separable?

**Answer:**

The perceptron learning algorithm does not terminate if the learning set is not linearly separable. To avoid infinite looping, specify finite number of iterations.

**Question: **

Would you call the PLA supervised or unsupervised learning?

**Answer:** Supervised learning

2. Obtain the linear regression of Y on X1, X2 (by hand-calculation or writing code, but do

not use an off-the-shelf linear regression function):

X1 X2 Y

0 0 2

0 1 2

1 0 10

1 1 10

**Question1:**

Write the regression equation.

**Question2: **

If we use the result of this regression as a classifier, what will be the equation of the classifier?

**3.** Exercises 3.6 and 3.7 from the textbook (page 92).

**4.** In least squares linear regression, we obtain the solution (the weights) analytically. In logistics regression, why don’t we analytically solve for the weights by setting the partial derivatives of the (log-)likelihood expression (or the negative of that expression) to zeros?

**5.**** **Explain the concepts of “training data,” “test data,” “training error,” and “test error.” Is it good to have as low a training error as possible?

**6.**Justify or refute: Gradient descent/ascent guarantees convergence to the global optimal solution.

**7.** Advice: Please read the textbook carefully. If you can devote further time, read the references.

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