ECS 271, Machine Learning: Homework #2 : (Updated)
Due: 15 April 2004

Please put the last 4 digits of your student ID on your answer sheets along with your name.

Instructor: Prof. Rao Vemuri, rvemuri@ucdavis.edu

(1) (50 points) The following data is given to you. You are asked to do the following things

(a) Build a decision tree and show the calculations on how you did it.

(b) Once you have the tree, you are asked to develop logical rules describing the conditions that result in sunburn

(c) Then simplify, if possible, the rule set and show the simplified rules.

Given Data

Independent Attributes / Condition Attributes

Dependent Attributes / Decision Attributes

Name	Hair	Height	Weight	Lotion	Result
Sarah	blonde	average	light	no	sunburned (positive)
Dana	blonde	tall	average	yes	none (negative)
Alex	brown	short	average	yes	none
Annie	blonde	short	average	no	sunburned
Emily	red	average	heavy	no	sunburned
Pete	brown	tall	heavy	no	none
John	brown	average	heavy	no	none
Katie	blonde	short	light	yes	none

Answer: A complete solution to this problem will appear elsewhere on the web site. First I will post the solution up to the building of the decision tree. A week later I will post the solution of the pruned tree.

 2. (20 points) Train a two-input, one-output Perceptron to simulate the Boolean AND function. The x0 input is always 1.

Start with three randomly selected weights w0, w1, and w2. in the range (0, 1). Repeatedly adjust the weights using the Perceptron learning rule until you get convergence. You may have to present the inputs several times before you see convergence. You are welcome to write a small piece of code as it may be tedious to do this manually. Plot the resulting straightline in the x1-x2 plane. Repeat with another randomly selected intiial weight set. Plot the resulting separating line. Did you get the same line?