MEENAKSHI COLLEGE OF ENGINEERING
No.12.Vembuli Amman koil Street, West K.K. Nagar,
Chennai – 600 078
Department of Artificial intelligence and Data science /
Artificial intelligence and Machine Learning
Model Examination
Programme & Batch:AI&DS, AI&ML Year/Semester: II/IV
Subject Code/Title : AL3451 – Machine Learning Date :13.05.25
Duration : 3Hrs Maximum : 100 marks
Instructions: Set :A
1. Before attempting any question paper, be sure that you got the correct question paper.
2. The missing data, if any, may be assumed suitably
3. Use the sketches wherever necessary
Course Outcomes
CO1: Explain the basic concepts of machine learning.
CO2: Construct supervised learning models.
CO3: Construct unsupervised learning algorithms.
CO4: Evaluate and compare different models
CO5: Analyze machine learning experiments using cross-validation and statistical tests.
K
Leve
Qn.
Question Mark l (CO)
No
(K1-
K6)
Part A (10 * 2 = 20 Marks) Answer all Questions
Define Machine Learning.
1 2 K1 CO1
Write some examples for machine learning applications.
2 2 K1 CO1
Mention the merits of Bayesian linear regression.
3 2 K1 CO2
Distinguish between Random Forest and Support Vector Machine.
4 2 K2 CO2
When is supervised learning better than unsupervised learning?
5 2 K1 CO3
Define Expectation Maximization.
6. 2 K1 CO3
7. Define Expectation Maximization. 2 K1 CO4
List the problems associated with Backpropagation Neural Network.
8. 2 K1 CO4
Recall the benefits of the Cross-Validation method.
9. 2 K1 CO5
10. How do you evaluate a Classification Algorithm 2 K1 CO5
Part B (5 * 13 = 65 Marks) Answer All Questions
Discuss the following
(i) Vapnik - Chervonenkis (VC) Dimension.(7)
11a) 13 K2 CO1
(ii) Probably Approximately Correct (PAC) Learning. (6)
OR
Write detailed notes on Inductive Bias and Bias variance trade-off.
11b) 13 K1 CO1
By the method of least squares find the straight line to the data given
12a) below. x 5 10 15 20 25 13 K5 CO2
y 16 19 23 26 30
OR
With an example explain the Decision Tree concepts in detail. CO2
12b) 13 K2
Compare Bagging, Boosting and Stacking ensemble methods. CO3
13a) 13 K2
OR
Cluster the following eight points with (x, y) representing locations) into
three clusters using K-means clustering method. A1(2, 10), A2(2, 5), A3(8,
13b) 13 K5 CO3
4), A4(5, 8), A5(7, 5), A6(6, 4), A7(1, 2), A8(4, 9).
Describe in brief about Multilayer perceptron activation functions.
14a) 13 K1 CO4
OR
Illustrate the following
14b) (i) Batch Normalization. (7) 13 K1 CO4
(ii) Dropout.(6)
With neat diagram explain about K-fold Cross Validation technique.
15a) 13 K1 CO5
OR
Elaborate the t test, McNemar's test and K-fold CV paired t test by giving
15b) your own example. 13 K2 CO5
Part C (1 * 15 = 15 Marks) Answer All Questions
16a) 15 K5 CO5
Consider the following list that contains name, age, gender and class of
sports. In the Gender field males are denoted by the numeric value 0 and
females by 1. Using the K-Nearest Neighbor (KNN) algorithm, find class of
sports for a girl whose name is Angelina, her k factor is 3, and her age is
Ajay 32 0 Football
Mark 40 0 Neither
Sara 16 1 Cricket
Zaira 34 1 Cricket
Sachin 55 0 Neither
Rahul 40 0 Cricket
Pooja 20 1 Neither
Smith 15 0 Cricket
Laxmi 55 1 Football
Michael 15 0 Football
OR
(b) The grades of a class of 9 students on a midterm report (X) and on
the final examination (Y) area as follows:
x 77 50 71 72 81 94 96 99 67
16b) 15 K5 CO1
y 82 66 78 34 47 85 99 99 68
(i) Estimate the linear regression line.
(ii) Estimate the final examination grade of a student who received a
grade of 85 on the midterm report.
Knowledge Level as per Bloom Taxonomy
K1- Remember; K2- Understand; K3- Apply; K4- Analyze; K5- Evaluate; K6- Create
Course Instructor Dept Exam cell I/c HoD
