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NPTEL Deep Learning Assignment 2 Answers 2023:
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Q.1. Suppose if you are solving a four class problem, how many discriminant function you will need for solving?
- 1
- 2
- 3
- 4
Q.2. Two random variable X1 and X2 follows Gaussian distribution with following mean and covariance.
X1~N (0, 3) and X2~N (0, 2). Which of following will is true.
- Distribution of X1 will be more flat than the distribution of X2.
- Distribution of X2 will be more flat than the distribution of X1.
- Peak of the both distribution will be same
- None of above.
Q.3. Which of the following is true with respect to the discriminant function for normal density.\?
- Decision surface is always orthogonal bisector to two surfaces when the covariance matrices of different classes are identical but otherwise arbitrary
- Decision surface is generally not orthogonal to two surfaces when the covariance matrices of different classes are identical but otherwise arbitrary
- Decision surface is always orthogonal to two surfaces but not bisector when the covariance matrices of different classes are identical but otherwise arbitrary
- Decision surface is arbitrary when the covariance matrices of different classes are identical but otherwise arbitrary
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Q.4. In which of following case the decision surface intersect the line joining two means of two class at midpoint? (Consider class variance is large relative to the difference of two means)
- When both the covariance matrices are identical and diagonal matrix.
- When the covariance matrices for both the class are identical but otherwise arbitrary.
- When both the covariance matrices are identical and diagonal matrix, and both the class has equal class probability.
- When the covariance matrices for both class are arbitrary and different.
Q.5. The decision surface between two normally distributed class w₁ and w2 is shown on the figure. Can you comment which of the following is true?
- Σi = 621, where Σi is covariance matrix of class i
- Σi = Σ, where Σ₁ is covariance matrix of class i
- Σi = arbitary, where Σi is covariance matrix of class i
- None of the above.
Q.6. For minimum distance classifier which of the following must be satisfied?
- All the classes should have identical covariance matrix and diagonal matrix.
- All the classes should have identical covariance matrix but otherwise arbitrary.
- All the classes should have equal class probability.
- None of above.
Q.7. You found your designed software for detecting spam mails has achieved an accuracy of 99%, i.e., it can detect 99% of the spam emails, and the false positive (a non-spam email detected as spam) probability turned out to be 5%. It is known that 50% of mails are spam mails. Now if an email is detected as spam, then what is the probability that it is in fact a non-spam email?
- 5/104
- 5/100
- 4.9/100
- 0.25/100
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Q.8. Which of the following statements are true with respect to K-NN classifier?
1. In case of very large value of k, we may include points from other classes into the neighbourhood. 2. In case of too small value of k the algorithm is very sensitive to noise. 3. KNN classifier classify unknown samples by assigning the label which is most frequent among the k nearest training samples.
- Statement 1 only
- Statement 1 and 2 only
- Statement 1, 2, and 3
- Statement 1 and 3 only
Q.9. You have given the following 2 statements, find which of these option is/are true in case of k NN?
4. In case of very large value of k, we may include points from other classes into the neighbourhood. 5. In case of too small value of k the algorithm is very sensitive to noise.
- 1
- 2
- 1 and 2
- None of this.
Q.10. The decision boundary of linear classifier is given by the following equation. 4x₁ + 6x₂ – 11 = 0
What will be class of the following two unknown input example? (Consider class 1 as positive class, and class 2 as the negative class) a2= [1, 2] a2= [1,1]
- a1 belongs to class 1, a2
- a2 belongs to class 1, a1 belongs to class 2 belongs to class 2
- a1 belongs to class 2, a2 belongs to class 2
- a1 belongs to class 1, a2 belongs to class 1
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Disclaimer: This answer is provided by us only for discussion purpose if any answer will be getting wrong don’t blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided by Chase2learn. This tutorial is only for Discussion and Learning purpose.
About NPTEL Deep Learning Course:
The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students will acquire the knowledge of applying Deep Learning techniques to solve various real life problems.
Course Layout:
- Week 1: Introduction to Deep Learning, Bayesian Learning, Decision Surfaces
- Week 2: Linear Classifiers, Linear Machines with Hinge Loss
- Week 3: Optimization Techniques, Gradient Descent, Batch Optimization
- Week 4: Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning
- Week 5: Unsupervised Learning with Deep Network, Autoencoders
- Week 6: Convolutional Neural Network, Building blocks of CNN, Transfer Learning
- Week 7: Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
- Week 8: Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization
- Week 9: Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.
- Week 10: Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.
- Week 11: LSTM Networks
- Week 12: Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam
CRITERIA TO GET A CERTIFICATE:
Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100
Final score = Average assignment score + Exam score
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.
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