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📌 Subject: Machine Learning
📌 Week: 12
📌 Session: NPTEL 2025
📌 Course Link: NPTEL Machine Learning
📌 Reliability: Expert-reviewed answers
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| Machine Learning Week 4 Answers |
| Machine Learning Week 5 Answers |
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| Machine Learning Week 12 Answers |
Introduction to Machine Learning Week 12 NPTEL Assignment Answers 2025
1. What is the VC dimension of the class of linear classifiers in 2D space?
- 2
- 3
- 4
- None of the above
Answer :- b
2. Which of the following learning algorithms does NOT typically perform empirical risk minimization?
- Linear regression
- Logistic regression
- Decision trees
- Support Vector Machines
Answer :- d
3. Statement 1: As the size of the hypothesis class increases, the sample complexity for PAC learning always increases.
Statement 2: A larger hypothesis class has a higher VC dimension.
Choose the correct option:
- Statement 1 is true. Statement 2 is true. Statement 2 is the correct reason for statement 1
- Statement 1 is true. Statement 2 is true. Statement 2 is not the correct reason for statement 1
- Statement 1 is true. Statement 2 is false
- Both statements are false
Answer :- b
4. When a model’s hypothesis class is too small, how does this affect the model’s performance in terms of bias and variance?
- High bias, low variance
- Low bias, high variance
- High bias, high variance
- Low bias, low variance
Answer :- a
5. Imagine you’re designing a robot that needs to navigate through a maze to reach a target. Which reward scheme would be most effective in teaching the robot to find the shortest path?
- +5 for reaching the target, -1 for hitting a wall
- +5 for reaching the target, -0.1 for every second that passes before the robot reaches the target
- +5 for reaching the target, -0.1 for every second that passes before the robot reaches the target, +1 for hitting a wall
- -5 for reaching the target, +0.1 for every second that passes before the robot reaches the target
Answer :- b
6. For each state, we define a variable that will store its value. The value of the state will help the agent determine how to behave later. First we will learn this value.
Let V be the mapping from state to its value.
Initially,
V(LE) = -1
V(X1) = V(X2) = V(X3) = V(X4) = V(Start) = 0
V(RE) = +1
For each state S∈{X1,X2,X3,X4,Start}, with SL being the state to its immediate left and
SR being the state to its immediate right, repeat:
V(S)=0.9×max(V(SL),V(SR))
Till V converges (does not change for any state).
What is V(X4) after one application of the given formula?
- 1
- 0.9
- 0.81
- 0
Answer :- b
7. What is V(X1) after one application of given formula?
- -1
- -0.9
- -0.81
- 0
Answer :- d
8. What is V(X1) after V converges?
- 0.59
- -0.9
- 0.63
- 0
Answer :- a
Conclusion:
In this article, we have uploaded the Introduction to Machine Learning Week 12 NPTEL Assignment Answers. These expert-verified solutions are designed to help you understand key concepts, simplify complex topics, and enhance your assignment performance. Stay tuned for weekly updates and visit www.answergpt.in for the most accurate and detailed solutions.
