07-18-2010, 03:20 PM
Deadline date:
Your assignment must be uploaded/submitted before or on July 20, 2010.
Assignment Marks: 20
Uploading instructions
Please view the assignment submission process document provided to you by the
Virtual University to upload the assignment.
Rules for Marking
It should be clear that your assignment will not get any credit if:
o The assignment is submitted after due date
o The assignment is copied
Objective
o To learn and understand basic concepts of Learning
Problems
Q 1) In your own words, define the core differences between supervised and
unsupervised learning. Marks: 5
Q 2) What is a common application of decision-tree learning? Marks: 2
Q 3) What issues can result from creating decision trees from training sets that are too
small or too large? Marks: 5
Q 4) If a concept learning process is started and instances in the given concept learning
problem are real numbers. There are two hypothesis spaces are given and their
hypothesis representation is given as follows: Marks: 8
I. For any real number “a”, every hypothesis can be written in the form x ≥ a
II. For any two real numbers “a” and “b”, every hypothesis can be written in the form
b ≥ x ≥ a
Answer the following questions:
• What is the size of hypothesis space I?
• What is the size of hypothesis space II?
• Consider classifying m distinct instances {x1, x2… xm}. How many different
ways can you classify the m instances by using hypothesis space I?
• Which hypothesis representation is more expressive?
Solution:
Your assignment must be uploaded/submitted before or on July 20, 2010.
Assignment Marks: 20
Uploading instructions
Please view the assignment submission process document provided to you by the
Virtual University to upload the assignment.
Rules for Marking
It should be clear that your assignment will not get any credit if:
o The assignment is submitted after due date
o The assignment is copied
Objective
o To learn and understand basic concepts of Learning
Problems
Q 1) In your own words, define the core differences between supervised and
unsupervised learning. Marks: 5
Q 2) What is a common application of decision-tree learning? Marks: 2
Q 3) What issues can result from creating decision trees from training sets that are too
small or too large? Marks: 5
Q 4) If a concept learning process is started and instances in the given concept learning
problem are real numbers. There are two hypothesis spaces are given and their
hypothesis representation is given as follows: Marks: 8
I. For any real number “a”, every hypothesis can be written in the form x ≥ a
II. For any two real numbers “a” and “b”, every hypothesis can be written in the form
b ≥ x ≥ a
Answer the following questions:
• What is the size of hypothesis space I?
• What is the size of hypothesis space II?
• Consider classifying m distinct instances {x1, x2… xm}. How many different
ways can you classify the m instances by using hypothesis space I?
• Which hypothesis representation is more expressive?
Solution:
Code:
Discuss it here until i upload solution