Reference no: EM132598595

Exercise 1:

Question 1. What is the difference between classification and regression? How are they similar?

Question 2. What is difference between supervised and unsupervised Learning with examples?

Question 3. The following table contains training examples that help predict whether a patient is likely to have a heart attack.

1). Using the heuristic of "selecting the attribute based on that it will best separate the samples into individual classes" to construct a minimal decision tree that predicts whether or not a patient is likely to have a heart attack. Show each step.
2). Do you need all the attributes for constructing this minimal decision tree? 3). Translate your decision tree into a collection of decision rules.

Exercise 2:

Question 1. What is a Perceptron and what is Multilayer perceptron? Illustrate the structure of a perceptron and multilayer perceptron.

Question 2. Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8): (i). Compute the Euclidean distance between the two objects.
(ii). Compute the Manhattan distance between the two objects.

Question 3. In the following dataset, 4 subjects belong to two different classes (A and B). Classify the new subject (Subject: Feature 1= 3; Feature 2=7; Class=?) using k nearest neighbour classification. Using Euclidean distance as distance function and the object is assigned to the majority class within the k nearest neighbour.

Perform kNN classification for the following values of k: (a). k = 1 (b). k = 3

Attachment:- Exercise.rar