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NPTEL Introduction to Machine Learning | Week 12 Assignment Answers 2024

Introduction to NPTEL Introduction to Machine Learning Course

NPTEL, the National Programme on Technology Enhanced Learning, is a joint initiative of the Indian Institutes of Technology (IITs) and the Indian Institute of Science (IISc). It offers online certification courses in various disciplines, including Machine Learning. Machine Learning, a subset of artificial intelligence, plays a crucial role in modern technology, powering applications from recommendation systems to self-driving cars.


Understanding Week 12 Assignment

Before diving into the Week 12 Assignment, let's recap what we've covered in previous weeks. The NPTEL Introduction to Machine Learning course typically covers topics such as:

NPTEL Introduction to Machine Learning | Week 12 Assignment Answers 2024




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Q1. Statement 1: Empirical error is always greater than generalisation error.
Statement 2: Training data and test data have different underlying(true) distributions.
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: 2. Statement 1 is true. Statement 2 is true. Statement 2 is not the correct reason for statement 1.
Q2. Let P(Ai)=2−i. Calculate the upper bound for P(⋃5i=1Ai) using union bound (rounded to 3 decimal places).
0.937
0.984
0.969
1
Answer: 3. 0.969
Q3. Which of the following is/are the shortcomings of TD Learning that Q-learning resolves?
TD learning cannot provide values for (state, action) pairs, limiting the ability to extract an optimal policy directly
TD learning requires knowledge of the reward and transition functions, which is not always available
TD learning is computationally expensive and slow compared to Q-learning
TD learning often suffers from high variance in value estimation, leading to unstable learning
TD learning cannot handle environments with continuous state and action spaces effectively
Answer:
Option 1. TD learning cannot provide values for (state, action) pairs, limiting the ability to extract an optimal policy directly
Q4. Given 100 hypothesis functions, each trained with 10^6 samples, what is the lower bound on the probability that there does not exist a hypothesis function with error greater than 0.1?
1 − 200e^−2⋅10^4
1 − 100e^10^4
1 − 200e^10^2
1 − 200e^−2⋅10^2
Answer:
Option 4. 1 − 200e^−2⋅10^2
Q5. The VC dimension of a pair of squares is:
3
4
5
6
Answer:
Option 2. 4
Q6. What is V(X4) after one application of the given formula?
1
0.9
0.81
0
Answer:
Option 2. 0.9
Q7. What is V(X1) after one application of the given formula?
-1
-0.9
-0.81
0
Answer:
Option 3. -0.81
Q8. What is V(X1) after V converges?
0.54
-0.9
0.63
0
Answer:
Option 1. 0.54
Q9. The behavior of an agent is called a policy. Formally, a policy is a mapping from states to actions. In our case, we have two actions: left and right. We will denote the action for our policy as A.
Clearly, the optimal policy would be to choose action right in every state. Which of the following can we use to mathematically describe our optimal policy using the learnt V?
For options (c) and (d), T is the transition function defined as: T(state, action) = next state. (more than one option may apply)
A={LeftRightifV(SL)>V(SR)otherwise
A={LeftRightifV(SR)>V(SL)otherwise
A=argmaxa({V(T(S,a))})
A=argmina({V(T(S,a))})
Answer:
Option 2. A={LeftRightifV(SR)>V(SL)otherwise
Q10. In games like Chess or Ludo, the transition function is known to us. But what about Counter Strike or Mortal Combat or Super Mario? In games where we do not know T, we can only query the game simulator with current state and action, and it returns the next state. This means we cannot directly argmax or argmin for V(T(S,a)). Therefore, learning the value function V is not sufficient to construct a policy. Which of these could we do to overcome this? (more than 1 may apply)
Assume there exists a method to do each option. You have to judge whether doing it solves the stated problem.
Directly learn the policy
Learn a different function which stores value for state-action pairs (instead of only state like V does)
Learn T along with V
Run a random agent repeatedly till it wins. Use this as the winning policy
Answer:
Options 1 and 2. Directly learn the policy and Learn a different function which stores value for state-action pairs (instead of only state like V does)






Regression and Classification Algorithms

Clustering Techniques

Neural Networks

Model Evaluation and Optimization

Week 12 focuses on advanced topics, building upon the foundational knowledge gained in earlier weeks. It may delve into complex algorithms or real-world applications of machine learning.


Week 12 Assignment Questions

Question 1: Description and Solution

The first question of the Week 12 Assignment may involve implementing a regression model to predict housing prices based on features such as location, square footage, and number of bedrooms.


Question 2: Description and Solution

Question 2 could revolve around classification techniques, perhaps requiring students to build a model to classify emails as spam or non-spam based on their content and features.


Question 3: Description and Solution

Question 3 may introduce clustering algorithms, tasking students with clustering a dataset into distinct groups based on similarities in features.


Question 4: Description and Solution

The final question might focus on neural networks, challenging students to design and train a neural network to recognize handwritten digits from images.


Tips for Completing Week 12 Assignment

To excel in the Week 12 Assignment, follow these tips:


Reviewing Lecture Notes: Go through the lecture notes and materials provided throughout the course, ensuring you understand the key concepts and techniques covered.


Understanding Key Concepts: Take the time to grasp the fundamental principles behind each algorithm and technique discussed in the course.


Practicing Previous Assignments: Practice solving similar problems from previous assignments or exercises to strengthen your understanding and skills.


Importance of Completing Assignments

Completing assignments is not just about earning grades; it's about reinforcing your learning and preparing yourself for real-world applications of machine learning. By tackling challenging problems and implementing solutions, you deepen your understanding and build confidence in your abilities.


Conclusion

In conclusion, the Week 12 Assignment of the NPTEL Introduction to Machine Learning course offers an opportunity to apply advanced concepts and techniques in practical scenarios. By approaching the assignment with diligence and enthusiasm, you can enhance your skills and advance your journey in the field of machine learning.


Unique FAQs:

How long do I have to complete the Week 12 Assignment?


Typically, assignments in NPTEL courses have a deadline specified in the course schedule. Be sure to check the deadline and plan your study accordingly.

Are the Week 12 Assignment questions similar to the quizzes and exercises we've done before?


While the questions may be related to the topics covered in previous quizzes and exercises, the Week 12 Assignment often involves more complex problems that require deeper understanding and application of concepts.

Can I collaborate with other students on the Week 12 Assignment?


NPTEL courses usually encourage individual work on assignments to ensure each student's understanding and mastery of the material. Collaboration may not be permitted unless explicitly stated otherwise by the course instructor.

Is it necessary to complete all questions in the Week 12 Assignment?


It's highly recommended to attempt all questions in the assignment to gain comprehensive knowledge and practice. However, if you encounter difficulties with a particular question, don't hesitate to seek help from course forums or instructors.

Will the Week 12 Assignment be graded?


Yes, assignments in NPTEL courses are typically graded to evaluate students' understanding and mastery of the course material. Be sure to submit your solutions before the deadline to receive feedback and grades.

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