We Discuss About That NPTEL IITKGP Introduction to Machine Learning Assignment 1 Answers
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NPTEL IITKGP Introduction to Machine Learning
ABOUT THE COURSE :
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.
Next Week Assignment Answers
This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.
- Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
- ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments).
- Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
- Proctored Exam score =50% of the proctored certification exam score out of 100
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50.
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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
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
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
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%
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
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
7. How many Boolean functions are possible with n features?
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
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”
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