We Discuss About That NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers
NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.
Are you looking for the Assignment Answers to NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers”
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.
BELOW YOU CAN GET YOUR NPTEL IITKGP Introduction to Machine Learning Assignment 3 Answers 2022? :
Q1. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.
Suppose, you want to predict the class of new data point x=1 and y=1 using euclidean distance in 3-NN. To which class the new data point belongs to?
B. – Class
C. Can’t say
D. None of these
2. Imagine you are dealing with a 10 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?
3. Fill in the blanks: K–Nearest Neighbor is a_ algorithm
A. Non-parametric, eager
B. Parametric, eager
C. Non-parametric, lazy
D. Parametric, lazy
4. Which of the following statements is True about the KNN algorithm?
A. KNN algorithm does more computation on test time rather than train time.
B. KNN algorithm does lesser computation on test time rather than train time.
C. KNN algorithm does an equal amount of computation on test time and train time.
D. None of these.
5. Which of the following necessitates feature reduction in machine learning?
A. Irrelevant and redundant features
B. Curse of dimensionality
C. Limited computational resources.
D. All of the above
6. When there is noise in data, which of the following options would improve the perfomance of the KNN algorithm?
A. Increase the value of k
B. Decrease the value of k
C. Changing value of k will not change the effect of the noise D. None of these
7. Find the value of the Pearson’s correlation coefficient of X and Y from the data in the following table.
8. Which of the following is false about PCA?
A. PCA is a supervised method
B. It identifies the directions that data have the largest variance
C. Maximum number of principal components = number of features
D. All principal components are othogonal to each other
9. In user-based collaborative filtering based recommendation, the items are recommended based on :
A. Similar users
B. Similar items
C. Both of the above
D. None of the above
10. Identify whether the following statement is true or false? “PCA can be used for projecting and visualizing data in lower dimensions.”