# NPTEL Introduction to Machine Learning – IITKGP Assignment 1 Answers

NPTEL Introduction to Machine Learning – IITKGP Assignment 1 Answers 2022:- In this post, We have provided answers of NPTEL Introduction to Machine Learning – IITKGP Assignment 1. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

## NPTEL Introduction to Machine Learning – IITKGP Assignment 1 Answers [July 2022]

Q1. Which of the following are classification tasks?

A. Find the gender of a person by analyzing his writing style
B. Predict the price of a house based on floor area, number of rooms etc.
C. Predict the temperature for the next day
D. Predict the number of copies of a book that will be sold this

`Answer:- a`

2. Which of the following is a not categorical feature?

A. Gender of a person
B. Height of a person
c. Types of Mountains
D. Nationality of a person

`Answer:- b`

3. Which of the following tasks is NOT a suitable machine leaming task?

A. Finding the shortest path between a pair of nodes in a graph
B. Predicting if a stock price will ise or fall
C. Predicting the price of petroleum
D. Grouping mails as spams or non-spams

`Answer:- a`

4. Suppose I have 10,000 emails in my mailbox out of which 200 are spams. The spam detection system detects 150 mails as spams, out of which 50 are actually spams. What is the precision and recall of my spam detection system?

A. Precision = 33.333%, Recall =25%
B. Precision = 25%, Recall = 33.33%
C. Precision = 33.33%, Recall = 75%
D. Precision = 75%. Recall = 33.33%

`Answer:- a`

5. A feature F1 can take certain values: A, B, C. D, E, F and represents the grade of students from a college. Which of the following statements is true in the following case?

A. Feature F1 is an example of a nominal variable.
B. Feature F1 is an example of ordinal variables.
C. It doesn’t belong to any of the above categories.
D. Both of these

`Answer:- b`

6. One of the most common uses of Machine Learning today is in the domain of Robotics. Robotic tasks include a multitude of ML methods tailored towards navigation, robotic control and a number of other tasks. Robotic control includes controlling the actuators available to the robotic system. An example of this is control of a painting arm in automotive industries. The robotic arm must be able to paint every corner in the automotive parts while minimizing the quantity of paint wasted in the process. Which of the following learning paradigms would you select for training such a robotic arm?

A. Supervised learning
B. Unsupervised learning
C. Combination of supervised and unsupervised learning
D. Reinforcement learning

`Answer:- d`

7. How many Boolean functions are possible with n features?

A. (22)n
B.(2n)
c (N2)
D (4N)

`Answer:- a`

8. What is the use of Validation dataset in Machine Leaming?

A. To train the machine learning model.
B. To evaluate the peformance of the machine learning model
C. To tune the hyperparameters of the machine learning model
D. None of the above

`Answer:- c`

9. Regarding bias and variance, which of the following statements are true? (Here ‘high’ and low’ are relative to the ideal model.)

A. Models which overfit have a high bias.
B. Models which overfit have a low bias.
C. Models which underfit have a high variance.
D. Models which underfit have a low variance

`Answer:- b, d`

10. Identify whether the following statement is true or false? Occam’s Razor is an example of Inductive Bias”

A. True
B. False

`Answer:- a`

## About Introduction To Machine Learning – IITKGP

This course provides a concise introduction to the fundamental concepts in machine learning and popular machine learning algorithms. We will cover the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour, an introduction to Bayesian learning and the naïve Bayes algorithm, support vector machines and kernels and neural networks with an introduction to Deep Learning. We will also cover the basic clustering algorithms. Feature reduction methods will also be discussed. We will introduce the basics of computational learning theory. In the course we will discuss various issues related to the application of machine learning algorithms. We will discuss hypothesis space, overfitting, bias and variance, tradeoffs between representational power and learnability, evaluation strategies and cross-validation. The course will be accompanied by hands-on problem solving with programming in Python and some tutorial sessions.

COURSE LAYOUT

• Week 1: Introduction: Basic definitions, types of learning, hypothesis space and inductive bias, evaluation, cross-validation
• Week 2: Linear regression, Decision trees, overfitting
• Week 3: Instance based learning, Feature reduction, Collaborative filtering based recommendation
• Week 4: Probability and Bayes learning
• Week 5: Logistic Regression, Support Vector Machine, Kernel function and Kernel SVM
• Week 6: Neural network: Perceptron, multilayer network, backpropagation, introduction to deep neural network
• Week 7: Computational learning theory, PAC learning model, Sample complexity, VC Dimension, Ensemble learning
• Week 8: Clustering: k-means, adaptive hierarchical clustering, Gaussian mixture model

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 6 assignments out of the total 8 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.