π Chandigarh University β MCA/BCA/B.Tech
These Mid-Semester papers are part of the official examinations of Chandigarh University,
designed to test knowledge and practical understanding of core MCA, Computer Applications, and B.Tech subjects.
π‘ Each paper encourages students to think critically, apply concepts, and showcase problem-solving skills β helping them prepare for real-world IT challenges.
Usage Condition:
- π Papers are for reference and study purposes only.
- π§βπ Students should use them responsibly and not for any malpractice.
- π Availability depends on the course and year.
Instructions
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This question paper consists of three sections. It is compulsory for students to attempt all questions of Section A and Section C.
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Students need to attempt any one question from question no. 12 and question no. 13 of Section C.
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Students have to attempt any one question from question no. 11 and question no. 10 of Section C.
Artificial Intelligence
Subject Code:24CAT-571
Semester: 3 | Time: 3 Hours | Max Marks: 60
Section A
| Q. No | Statement | CO | BT Level |
|---|---|---|---|
| 1 | Discuss techniques that can be used for identifying outliers. | CO1 | 2 |
| 2 | Define K-Nearest Neighbors. | CO1 | 1 |
| 3 | Define reinforcement learning. | CO4 | 1 |
| 4 | List the main components of TensorFlowβs architecture. | CO5 | 1 |
| 5 | Define Artificial Neural Network. | CO5 | 2 |
Section B
| Q. No | Question |
|---|---|
| 6 | Provide a comprehensive explanation of linear regression and the approaches used to determine the line of best fit. |
| 7 | Differentiate between TP (true positive) and FP (false positive) in the context of the confusion matrix with an example. |
| 8 | Compare CNN and ANN architectures in the context of reinforcement learning. |
| 9 | Solve a linear regression problem using TensorFlow. |
| 10 | Compare supervised, unsupervised, and reinforcement learning techniques. Discuss scenarios where each approach is most effective. |
Section C-(3*10)
| Q. No | Question |
|---|---|
| 11 | Design a classification machine learning model using a dataset of your choice. |
| 12 | Explain how reinforcement learning differs from supervised and unsupervised learning. Provide an example to illustrate its advantages and challenges. |
| Q. No | Question |
|---|---|
| 13 | Write short notes on the following: |
| (i) TensorFlow and its library | |
| (ii) CNN and ANN |
