AKTU Machine Learning Techniques (KCS055) Question Paper 2021-22

Note: Attempt all Sections. If require any missing data; then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

1. What is a “Well -posed Learning “problem? Explain with an example.
2. What is Occam's razor in ML?
3. What is the role of Inductive Bias in ANN?
4. What is gradient descent delta rule?
5. What is Paired t Tests in Hypothesis evaluation?
6. How do you find the confidence interval for a hypothesis test?
7. What is sample complexity of a Learning Problem?
8. Differentiate between Lazy and Eager Learning
9. What is the problem of crowding in GA
10. Comparison of purely analytical and purely inductive learning.

Section B

Q2. Attempt any three of the following

1. Design the Final design of checkers learning program.
2. What is Maximum Likelihood and Least Squared Error Hypothesis?
3. What problem does the EM algorithm solve
4. Highlight the importance of Case Based Learning
5. Write short notes on Learning First Order Rules

Section C

Q3

Attempt any one part of the following

1. Explain the “Concept Learning” Task Giving an example
2. Find the maximally general hypothesis and maximally specific hypothesis for the training examples given in the table using the candidate elimination algorithm. Given Training Example : Sky Temp Humidity wind water Forecast sport Sunny warm Normal Strong warm same Yes Sunny warm High Strong warm same Yes Rainy cold High Strong warm change No Sunny warm High Strong cool change Yes

Q4

Attempt any one part of the following

1. Comment on the Algorithmic convergence & Generalization property of ANN.
2. Discuss the following issues in Decision Tree Learning:
   1. Overfitting the data
   2. Guarding against bad attribute choices
   3. Handling continuous valued attributes
4. Handling missing attribute values
   5. Handling attributes with differing costs

Q5

Attempt any one part of the following

1. How is Naïve Bayesian Classifier different from Bayesian Classifier?
2. Explain the role of Central Limit Theorem Approach for deriving Confidence Interval.

Q6

Attempt any one part of the following

1. Write short notes on Probably Approximately Correct (PAC) learning model.
2. Discuss various Mistake Bound Model of Learning

Q7

Attempt any one part of the following

1. What is the significance of Learn -one Rule Algorithm?
2. Describe a prototypical genetic algorithm along with various operations possible in it.

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