# Introduction to Machine Learning Week 6 Solutions NPTEL

This set of MCQ(multiple choice questions) focuses on the Introduction to Machine learning Week 6 Solutions.

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.

### Course layout

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### Introduction to Machine learning Week 6 Solutions

Q1-2 data given

Q1. Find the appropriate weights for w0, w1 and w2 to represent the AND function. Threshold function = {1, if output>0; 0 otherwise}. x0 and x1 are the inputs and b1 = 1 is the bias.

a) w0=1, w1=1, w2=1
b) w0=1, w1=1, w2=-1
c) w0=-1, w1=-1, w2=-1
d) w0=2, w1=-2, w3=-1

Introduction to Machine Learning Week 6 Solutions

Q2. Fill in the correct weights to represent OR function:

a) w0=1, w1=1, w2=0
b) w0=1, s2=1, w3=1
c) w0=1, w1=1, w2=-1
d) w0=-1, w1=-1, w2=-1

Introduction to Machine Learning Week 6 Solutions

Q3. Which of the following gives non-linearity to a neural network.

b) Bias
c) ReLU Activation Function
d) None

Q4. Suppose you are to design a system where you want to perform word prediction also known as language modeling. You are to take the output from the previous state and also the input at each step to predict the next word. The inputs at each step are the words for which the next words are to be predcited. Which of the following neural network would you use?

a) Multi-Layer Perception
b) Recurrent Neural Network
c) Convolutional Neural Network
d) Perception

Introduction to Machine Learning Week 6 Solutions

Q5. For a fully-connected deep network with one hidden layer, increasing the number of hidden units should have what effect on bias and variance?

a) Decrease bias, increase variance
b) Increase bias, increase variance
c) Increase bias, decrease variance
d) No change

Answer: a) Decrease bias, increase variance

Introduction to Machine Learning Week 6 Solutions

Q6-7 with data provided

Q6. How many neurons should you have at the output?

a) 3
b) 2
c) 1
d) 4

Q7. What should be the loss function used to train the model?

a) Multi-Class Cross-Entropy Loss
b) Mean Squared Error
c) Binary Cross-Entropy Loss

Introduction to Machine Learning Week 6 Solutions

Q8. A Convolutional Neural Network (CNN) is a Deep Neural Network that can extract various abstract features from an input required for a given task. Given the operations performed by a CNN on an input:
1) Max Pooling
2) Convolution Operation
3) Flatten
4) Forward propagation by Fully Connected Network
Identify the correct sequence from the options below:

a) 4, 3, 2, 1
b) 2, 1, 3, 4
c) 3, 1, 2, 4
d) 4, 2, 1, 3

Answer: b) 2, 1, 3, 4

Introduction to Machine Learning Week 6 Solutions

Q9. An autoencoder is a Neural Network architecture used to create lower dimensional input representation. Which of the following statements are true about it?

a) It is an unsupervised algorithm similar to PCA
b) It can generate new data by learning the probability distribution
c) Its target output is the input
d) Autoencoders have linear encoder and decoder

Q10. In a simple MLP model with 8 neurons in the input layer, 5 neurons in the hidden layer and 1 neuron in the output layer. What is the size of the weight matrices between hidden to output layer and input to hidden layer?

a) [5 X 1], [8 X 5]
b) [8 X 5], [1 X 5]
c) [3 X 1], [3 X 3]
d) [3 X 3], [3 X 1]

Answer: a) [5 X 1], [8 X 5]

Introduction to Machine Learning Week 6 Solutions

Q11. If you increase the number of hidden layers in a Multi-Layer Perceptron, the classification error of test data always decreases. True or False?

a) True
b) False

Q12. Which of the following represents the range of output values for a sigmoid function?

a) -1 to 1
b) –âˆž to âˆž
c) 0 to 1
d) 0 to âˆž

Introduction to Machine Learning Week 6 Solutions

Q13. A single perceptron can compute the XOR function

a) True
b) False

Q14. What are the steps for using a gradient descent algorithm?
1. Calculate error between the actual value and the predicted value.
2. Repeat until you find the best weights of network.
3. Pass an input through the network and get values from output layer.
4. Initialize random values for weight and bias.
5. Go to each neuron which contributes to the error and change its respective values to reduce the error.

a) 4, 3, 1, 5, 2
b) 1, 2, 3, 4, 5
c) 3, 4, 5, 2, 1
d) 2, 3, 4, 5, 1

Answer: a) 4, 3, 1, 5, 2

Introduction to Machine Learning Week 6 Solutions

Q15. The back=propagation learning algorithm applied to a two-layer neural network

a) always finds the globally optimal solution
b) finds a locally optimal solution which may be globally optimal
c) never finds the globally optimal solution
d) finds a locally optimal solution which is never globally optimal

Answer: b) finds a locally optimal solution which may be globally optimal

Introduction to Machine Learning Week 6 Solutions

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