AKTU Machine Learning (KOE073) Question Paper 2022-23

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

Section A

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

1. Define Machine learning and its application.
2. What do you mean by big data analysis
3. Define Binary Decision Tree
4. Discuss the weight and bias in ANN
5. How do you evaluate hypothesis?
6. What is Bayes Theorem?
7. What is finite and infinite hypothesis space?
8. What is PAC learning model in machine learning?
9. What is the meaning of Chromosome in genetic algorithm?
10. What is the difference between reinforcement learning and Artificial Intelligence?

Section B

Q2. Attempt any three of the following · 10 × 3 = 30 marks

1. What are the five limitations of machine learning? Explain with suitable example.
2. Define the following in decision tree algorithm: (i) Entropy, (ii) Information gain, (iii) Gini index, (iv) Gain Ratio, (iv) Chi-Square
3. What is Naive Bayes classifier and how does it work? Explain the advantages of Naive Bayes algorithm.
4. What is the advantages & disadvantages of locally weighted regression? Explain with suitable example.
5. Explain the main steps of genetic algorithm with suitable examples?

Section C

Q3

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain the difference between Find-S and candidate elimination algorithm?
2. What is the inductive bias in CNN? Explain the image identification with CNN.

Q4

Attempt any one part of the following · 10 × 1 = 10 marks

1. What is the difference between forward propagation and backward propagation in neural networks explain weight calculation for forward pass network?
2. Explain the steps of decision tree making, with following data set, also calculate the Calculate data set entropy and information gain.
   Play
   DAY Outlook Temperature Humidity Sun light cricket
   D1 Rainy Hot High Weak No
D2 Rainy Hot High strong No
   D3 Overcast Hot High Weak Yes
   D4 Sunny Mild High Weak Yes
   D5 Sunny Cool Normal Weak Yes

Q5

Attempt any one part of the following · 10 × 1 = 10 marks

1. Show the following data set through Naive Bayes Classifiers:
   (i) If the weather is sunny, then the Player should play or not?
   (ii) If the level of humidity is medium & spend of the wind is high and then then the plays should play or not?
   Case Outlook Temperature Humidity Windy Play Golf
   0 Rainy Hot High FALSE No
1 Rainy Hot High TRUE No
   2 Overcast Hot High FALSE Yes
   3 Sunny Mild High FALSE Yes
   4 Sunny Cool Normal FALSE Yes
   5 Sunny Cool Normal TRUE No
   6 Overcast Cool Normal TRUE Yes
   7 Rainy Mild High FALSE No
   8 Rainy Cool Normal FALSE Yes
   9 Sunny Mild Normal FALSE Yes
   10 Rainy Mild Normal TRUE Yes
   11 Overcast Mild High TRUE Yes
   12 Overcast Hot Normal FALSE Yes
   13 Sunny Mild High TRUE No
2. Draw the cluster of following 8 points into 3 clusters:
   A1=(5,10), A2=(4,6), A3=(9,4), A4=(5,8), A5=(7,5), A6=(7,4), A7=(3,2), A8=(4,9).
   Use the k-means algorithm and Euclidean distance and take the Initial cluster centers are
   A2(4, 6), A4(5, 8) & A8 (4,9). The solution up to two iterations.

Q6

Attempt any one part of the following · 10 × 1 = 10 marks

1. Show the application of Clustering in various sectors, discus with following examples: Marketing, Insurance, & Earth-quake studies.
2. Show the application of supervised machine learning, explain with suitable example.

Q7

Attempt any one part of the following · 10 × 1 = 10 marks

1. What are the 4 types of reinforcement? Explain any two.
2. Is reinforcement learning Artificial intelligence or Machine Learning? also explain the data mining in ML.

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