nptel machine learning assignment 3 solutions 2023

Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023

This set of MCQ(multiple choice questions) focuses on the Introduction to Machine Learning NPTEL Week 3 Solutions NPTEL 2023 .

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.

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Introduction to Machine learning NPTEL 2023 Week 3 Solutions

Q1. Fill in the blanks: K-Nearest Neighbor is a _______, ________ algorithm

a) Non-parametric, eager b) Parametric, eager c) Non-parametric, lazy d) Parametric, lazy

Q2. You have been given the following 2 statements. Find out which of these options is/are true in the case of k-NN. (i) In case of very large value of k, we may include points from other classes in to the neighborhood. (ii) In case of too small value of k, the algorithm is very sensitive to noise.

a) (i) is True and (ii) is False b) (i) is False and (ii) is True c) Both are True d) Both are False

Q3. State whether the statement is True/False: k-NN algorithm does more computation on test time rather than train time.

a) True b) False

Introduction to Machine Learning NPTEL Week 3 Solutions

Q4. Suppose you are given the following images(1 represents the left image, 2 represents the middle and 3 represents the right). Now you task is to find out the value of k in k-NN in each of the images shown below. Here k1 is for 1st, k2 is for 2nd and k3 is for 3rd figure.

a) k1 > k2 > k3 b) k1 < k2 > k3 c) k1 < k2 < k3 d) None of these

Q5. Which of the following necessitates feature reduction in machine learning?

a) Irrelevant and redundant features b) Limited training data c) Limited computational resources d) All of the above

Q6. Suppose, you have given the following data where x and y are the 2 input variables and Class is the dependent variable.

a) + Class b) – Class c) Can’t say d) None of these

Q7. What is the optimum number of principal components in the below figure?

a) 10 b) 20 c) 30 d) 40

Q8. Suppose we are using dimensionality reduction as pre-processing technique, i.e, instead of using all the features, we reduce the data to k dimensions with PCA. And then use these PCA projections as our features. Which of the following statements is correct? Choose which of the options is correct?

a) Higher value of ‘k’ means more regularization b) Higher value of ‘k’ means less regularization

Q9. In 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

Q10. The major limitation of collaborative filtering is:

a) Cold start b) Overspecialization c) None of the above

Q11. Consider the figures below. Which figure shows the most probable PCA component directions for the data points?

a) A b) B c) C d) D

Q12. Suppose that you wish to reduce the number of dimensions of a given data to dimensions using PCA. Which of the following statement is correct?

a) Higher means more regularization b) Higher means less regularization c) Can’t say

Q13. Suppose you are given 7 plots 1-7 (left to right) and you want to compare Pearson correlation coefficients between variables of each plot. Which fo the following is true? 1. 1 < 2<3<4 2. 1>2>3>4 3. 7<6<5<4 4. 7>6>5>4

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

Q14. Imagine you are dealing with 20 class classification problem. What is the maximum number of discriminant vectors that can be produced by LDA?

a) 20 b) 19 c) 21 d) 10

Q15. In which of the following situations collaborative filtering algorithm is appropriate?

a) You manage an online bookstore and you have the book ratins from many users. For each user, you want to recommend other books he/she will like based on her previous ratins and other users’ ratings. b) You manage an online bookstore and you have the book raings from many users. You want to predict the expected sales volume(No of books sold) as a function of average rating of a book. c) Both A and B d) None of the above

Q1. Which of the following is false about a logistic regression based classifier?

a)  The logistic function is non-linear in the weights b) The logistic function is linear in the weights c) he decision boundary is non-linear in the weights d) The decision boundary is linear in the weights

Answer: a,c

Q2. Consider the case where two classes follow Gaussian distribution which are centered at (3, 9) and (−3, 3) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

a) y−x=3 b) x+y=3 c) x+y=6 d) both (b) and (c) e) None of the above f) Can not be found from the given information

Q3. Consider the following relation between a dependent variable and an independent variable identified by doing simple linear regression. Which among the following relations between the two variables does the graph indicate?

a)  as the independent variable increases, so does the dependent variable b) as the independent variable increases, the dependent variable decreases c) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding increase d) if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding decrease e)  the dependent variable in this graph does not actually depend on the independent variable f) none of the above

Q4. Given the following distribution of data points:

What method would you choose to perform Dimensionality Reduction?

a) Linear Discriminant Analysis b) Principal Component Analysis

Q5. In general, which of the following classification methods is the most resistant to gross outliers?

a) Quadratic Discriminant Analysis (QDA) b) Linear Regression c) Logistic regression d) Linear Discriminant Analysis (LDA)

Q6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y=m+X2+c=+2+. Available to us are samples of the variables X and Y. Is it possible to apply linear regression to this data to estimate the values of m and c?

a) no b) yes c) insufficient information

Q7. In a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y|x| follows a

a) Bernoulli distribution  b) binomial distribution  c) normal distribution  d) exponential distribution

Q8. Consider the following data:

Assuming that you apply LDA to this data, what is the estimated covariance matrix?

a) [1.8750.31250.31250.9375][1.8750.31250.31250.9375] b) [2.50.41670.41671.25] c) [1.8750.31250.31251.2188] d) [2.50.41670.41671.625] e) [3.251.16671.16672.375] f) [2.43750.8750.8751.7812] g) None of these

Q9. Given the following 3D input data, identify the principal component.

(Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

a) ⎢−0.10220.00180.9948⎤⎦⎥ b) ⎡⎣⎢0.5742−0.81640.0605⎤⎦⎥  c) ⎢0.57420.81640.0605⎤⎦⎥ d) ⎡⎣⎢−0.57420.81640.0605⎤⎦ e) ⎡⎣⎢0.81230.57740.0824⎤⎦⎥ f) None of the above

Q10. For the data given in the previous question, find the transformed input along the first two principal components.

a) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢0.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142−0.0196−0.1181−0.11630.5702−0.72570.27270.1724−0.0355⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ b) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.72250.89440.79590.79771.48420.18841.18681.08640.8785⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ c) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢−6.2814−4.3143−3.7368−1.79502.29173.52894.91865.38830.6100−0.4487−1.26511.33450.5474−1.0250−1.26721.5142⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥  d) ⎡⎣⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢1.47213.43924.01665.958410.045111.282312.672013.1418−0.1817−1.2404−2.05680.5428−0.2443−1.8167−2.05890.7225⎤⎦⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥ e) None of the above

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NPTEL Introduction to Machine Learning Assignment 3 Answers 2023

Hello Learners, In this Post, you will find NPTEL Introduction to Machine Learning Assignment 3 Week 3 Answers 2023 . All the Answers are provided below to help the students as a reference don’t straight away look for the solutions.

NPTEL Introduction to Machine Learning Assignment 4 Answers👇

Note: First try to solve the questions by yourself. If you find any difficulty, then look for the solutions.

NPTEL Introduction to Machine Learning Assignment 3 Answers 2023:

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Q.1. Which of the following is false about a logistic regression based classifier?

• The logistic function is non-linear in the weights
• The logistic function is linear in the weights
• The decision boundary is non-linear in the weights
• The decision boundary is linear in the weights

Q.2. Consider the case where two classes follow Gaussian distribution which are cen- tered at (3, 9) and (−3, 3) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

• both (b) and (c)
• None of the above
• Can not be found from the given information

Q.3. Consider the following relation between a dependent variable and an independent variable identified by doing simple linear regression. Which among the following relations between the two variables does the graph indicate?

• as the independent variable increases, so does the dependent variable
• as the independent variable increases, the dependent variable decreases
• if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding increase
• if an increase in the value of the dependent variable is observed, then the independent variable will show a corresponding decrease
• the dependent variable in this graph does not actually depend on the independent variable
• none of the above

NPTEL Introduction to Machine Learning Assignment 4 Answers Join Group👇

Q.4. Given the following distribution of data points:

What method would you choose to perform Dimensionality Reduction?

• Linear Discriminant Analysis
• Principal Component Analysis

Q.5. In general, which of the following classification methods is the most resistant to gross outliers?

• Quadratic Discriminant Analysis (QDA)
• Linear Regression
• Logistic regression
• Linear Discriminant Analysis (LDA)

Q.6. Suppose that we have two variables, X and Y (the dependent variable). We wish to find the relation between them. An expert tells us that relation between the two has the form Y=m+X2+c. Available to us are samples of the variables X and Y. Is it possible to apply linear regression to this data to estimate the values of m and c ?

• insufficient information

NPTEL Introduction to Machine Learning Week 3 Answers Join Group👇

Q.7. in a binary classification scenario where x is the independent variable and y is the dependent variable, logistic regression assumes that the conditional distribution y|x follows a.

• Bernoulli distribution
• binomial distribution
• normal distribution
• exponential distribution

Q.8. Consider the following data:

Assuming that you apply LDA to this data, what is the estimated covariance matrix?

• Answer: e. [3.251.16671.16672.375]

Q.9. Given the following 3D input data, identify the principal component.

(Steps: center the data, calculate the sample covariance matrix, calculate the eigenvectors and eigenvalues, identify the principal component)

• Answer: e. [0.8123 0.5774 0.0824]

Q.10. For the data given in the previous question, find the transformed input along the first two principal components.

Nptel introduction to machine learning assignment 3 answers join group👇.

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  Brokenprogrammers . This tutorial is only for Discussion and Learning purpose.

About NPTEL Introduction to Machine Learning Course:

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:

• Week 0:  Probability Theory, Linear Algebra, Convex Optimization – (Recap)
• Week 1:  Introduction: Statistical Decision Theory – Regression, Classification, Bias Variance
• Week 2:  Linear Regression, Multivariate Regression, Subset Selection, Shrinkage Methods, Principal Component Regression, Partial Least squares
• Week 3:  Linear Classification, Logistic Regression, Linear Discriminant Analysis
• Week 4:  Perceptron, Support Vector Machines
• Week 5:  Neural Networks – Introduction, Early Models, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation – MLE, MAP, Bayesian Estimation
• Week 6:  Decision Trees, Regression Trees, Stopping Criterion & Pruning loss functions, Categorical Attributes, Multiway Splits, Missing Values, Decision Trees – Instability Evaluation Measures
• Week 7:  Bootstrapping & Cross Validation, Class Evaluation Measures, ROC curve, MDL, Ensemble Methods – Bagging, Committee Machines and Stacking, Boosting
• Week 8:  Gradient Boosting, Random Forests, Multi-class Classification, Naive Bayes, Bayesian Networks
• Week 9:  Undirected Graphical Models, HMM, Variable Elimination, Belief Propagation
• Week 10:  Partitional Clustering, Hierarchical Clustering, Birch Algorithm, CURE Algorithm, Density-based Clustering
• Week 11:  Gaussian Mixture Models, Expectation Maximization
• Week 12:  Learning Theory, Introduction to Reinforcement Learning, Optional videos (RL framework, TD learning, Solution Methods, Applications)

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.

If you have not registered for exam kindly register Through https://examform.nptel.ac.in/

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NPTEL Social Networks Assignment 8 Answers 2023

NPTEL Introduction to Machine Learning Assignment 3 Answers 2023

NPTEL Introduction to Machine Learning Assignment 3 Answers 2023:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 3 You must submit your assignment to your own knowledge.

NPTEL Introduction To Machine Learning Week 3 Assignment Answer 2023

1. Which of the following are differences between LDA and Logistic Regression?

• Logistic Regression is typically suited for binary classification, whereas LDA is directly applicable to multi-class problems
• Logistic Regression is robust to outliers whereas LDA is sensiti v e to outliers
• both (a) and (b)
• None of these

2. We have two classes in our dataset. The two classes have the same mean but different variance.

LDA can classify them perfectly. LDA can NOT classify them perf e ctly. LDA is not applicable in data with these properties Insufficient information

3. We have two classes in our dataset. The two classes have the same variance but different mean.

LDA can classify them perfectly. LDA can NOT classify them perfectly. LDA is not applicable in data with these prop e rties Insufficient information

4. Given the following distribution of data points:

What method would you choose to perform Dimensionality Red u ction? Linear Discriminant Analysis Principal Component Analysis Both LDA and/or PCA. None of the above.

5. If log(1−p(x)/1+p(x))=β0+βx Wha t is p(x) ?

p(x)=1+eβ0+βx / eβ0+βx p(x)=1+eβ0+βx / 1−eβ0+βx p(x)=eβ0+βx / 1+eβ0+βx p(x)=1−eβ0+βx / 1+eβ0+βx

Red Orange Blue Green

7. Which of these techniques do we use to optimise Logistic Regres s ion:

Least S q uare Error Maximum Likelihood (a) or (b) are equally good (a) and (b) perform very poorly, so we generally avoid using Logistic Regression None of these

8. LDA assumes that the class data is distributed as:

Poisson Unif o rm Gaussian LDA makes no such assumption.

9. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y=meX+c. Suppose the samples of the variables X and Y are available to us. Is it possible to apply linear regression to this data to estimate the values of m and c ?

No. Yes. Insufficient information. None of the above.

10. What might happen to our logistic regression model if the number of features is more th a n the number of samples in our dataset?

It will remain unaffected It will not find a hyperplane as the decision bound a ry It will over fit None of the above

NPTEL Introduction to Machine Learning Assignment 3 Answers [July 2022]

1. For linear classification we use: a. A linear function to separate the classes. b . A linear function to model the data. c. A linear loss. d. Non-linear function to fit the data.

2. Logit transformation for Pr(X=1) for given data is S=[0,1,1 , 0,1,0,1] a. 3/4 b. 4/3 c. 4/7 d . 3/7

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3. The output of binary class logistic regression lies in this range. a. [−∞,∞] b. [−1,1] c. [0,1] d. [−∞ , 0]

4. If log(1−p(x)1+p(x))=β0+βxlog What is p(x)p(x)?

5. Logistic regression is robust to outliers. Why? a . The squashing of output values between [0, 1] dampens the affect of outliers. b. Linear models are robust to outliers. c. The parameters in logistic regression tend to take small values due to the nature of the problem setting and hence outliers get translated to the same range as other samples. d. The given statement is false.

6. Aim of LDA is (multiple options may apply) a. Minimize intra-class variability. b. Maximize intra-class variability. c . Minimize the distance between the mean of classes d. Maximize the distance between the mean of classes

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7. We have two classes in our dataset with mean 0 and 1, and variance 2 and 3. a. LDA may be able to classify them perfectly. b. LDA will definitely be able to classify them perfectly. c. LDA will definitely NOT be able to classify them perfectly . d. None of the above.

8. We have two classes in our dataset with mean 0 and 5 , and variance 1 and 2. a. LDA may be able to classify them perfectly. b. LDA will definitely be able to classify them perfectly. c. LDA will definitely NOT be able to classify them perfectly. d. None of the above.

9. For the two classes ’+ ’ and ’-’ shown below. While performing LDA on it, which line is the most appropriate for projecting data points? a. Red b. Orange c. Blue d. Green

10. LDA assumes that the class data is distributed as: a. Poisson b. Uniform c. Gaussian d. LDA makes no such assumption .

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What is Introduction to Machine Learning?

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.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of the 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 THE 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.

NPTEL Introduction to Machine Learning Assignment 3 Answers [Jan 2022]

Q1. consider the case where two classes follow Gaussian distribution which are centered at (6, 8) and (−6, −4) and have identity covariance matrix. Which of the following is the separating decision boundary using LDA assuming the priors to be equal?

(A) x+y=2 (B) y−x=2 (C) x=y (D) both (a) and (b) (E) None of the above (F) Can not be found from the given information

Answer:- (A) x+y=2

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Q2. Which of the following are differences between PCR and LDA?

(A) PCR is unsupervised whereas LDA is supervised (B) PCR maximizes the variance in the data whereas LDA maximizes the separation between the classes (C) both (a) and (b) (D) None of these

Answer:- (A) PCR is unsupervised whereas LDA is supervised

Q3. Which of the following are differences between LDA and Logistic Regression?

(A) Logistic Regression is typically suited for binary classification, whereas LDA is directly applicable to multi-class problems (B) Logistic Regression is robust to outliers whereas LDA is sensitive to outliers (C) both (a) and (b) (D) None of these

Answer:- (C) both (a) and (b)

ALSO READ :- NPTEL Registration Steps [July – Dec 2022] NPTEL Exam Pattern Tips & Top Tricks [2022] NPTEL Exam Result 2022 | NPTEL Swayam Result Download

Q4. We have two classes in our dataset. The two classes have the same mean but different variance.

• LDA can classify them perfectly.
• LDA can NOT classify them perfectly.
• LDA is not applicable in data with these properties
• Insufficient information

Answer:- 2. LDA can NOT classify them perfectly.

Q5. We have two classes in our dataset. The two classes have the same variance but different mean.

Answer:- 1. LDA can classify them perfectly.

Q6. Which of these techniques do we use to optimise Logistic Regression:

• Least Square Error
• Maximum Likelihood
• (a) or (b) are equally good
• (a) and (b) perform very poorly, so we generally avoid using Logistic Regression

Answer:- 2.Maximum Likelihood

Q7. Suppose we have two variables, X and Y (the dependent variable), and we wish to find their relation. An expert tells us that relation between the two has the form Y = meX + c . Suppose the samples of the variables X and Y are available to us. Is it possible to apply linear regression to this data to estimate the values of m and c ?

• insufficient information

Answer:- 2.yes

Q8. What might happen to our logistic regression model if the number of features is more than the number of samples in our dataset?

• It will remain unaffected
• It will not find a hyperplane as the decision boundary
• It will overfit
• None of the above

Answer:- 3. It will overfit

Q9. Logistic regression also has an application in

• Regression problems
• Sensitivity analysis
• Both (a) and (b)

Answer:- 3. Both (a) and (b)

Q10. Consider the following datasets:

Which of these datasets can you achieve zero training error using Logistic Regression (without any additional feature transformations)?

• Both the datasets
• Only on dataset 1
• Only on dataset 2
• None of the datasets

Answer:- For Answer Click Here

NPTEL Introduction to Machine Learning Assignment 3 Answers 2022:- In This article, we have provided the answers of Introduction to Machine Learning Assignment 3

Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.

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Category: NPTEL

The joy of computing using python | week 11, quantum mechanics 1 | week 11, programming in modern c++ | week 11, programming in java | week 11, leadership and team effectiveness | week 11, introduction to machine learning | week 11, cloud computing | week 11, blockchain and its applications | week 11, an introduction to artificial intelligence | week 11, introduction to internet of things week 10.

Introduction to Machine Learning

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

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Course layout, books and references.

• The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, Jerome H. Friedman (freely available online)
• Pattern Recognition and Machine Learning, by Christopher Bishop (optional)

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Prof. Balaraman Ravindran

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List of International Airports in Russia

Russia is the largest country in the world, stretching across two continents. It has more than 2000 airports and among them over 50 are international airports.

In this article, we will discuss the location, hub, history architecture, and elevation from sea level of a list of ten International Airports in Russia.

A list of the international airports along with the passengers handled by them in 20/.23 is described in the following table.

Sheremetyevo International Airport

Sheremetyevo Alexander S. Pushkin International Airport is one of four international airports serving Moscow. It is Russia’s busiest airport. In Europe, it is the thirteenth busiest airport. Sheremetyevo was first constructed as a military airbase and later converted into a civilian airport in 1959.

• Six terminals i.e. Terminal A, Terminal B, Terminal C, Terminal D, Terminal E, and Terminal F make up the airport: one domestic terminal, one private aviation terminal, and four international terminals.
• About 49.9 million passengers passed through the airport in 2019, of whom 26 million were international and 23 million were Russian. In 2019, 386,370 aircraft passed through the airport. However, the most recent data indicates that 28.4 million passengers traveled in 2022.
• For public access, there are rail, interterminal underground, bus and road services.
• Aeroexpress, a division of Russian Railways, runs a nonstop route that links the airport to Moscow’s central Belorussky station.
• The Aeroexpress train station, Terminals D, E, and F, and Terminals B and C are all connected by the airport’s Automated Passenger Transportation System (APTS).
• The municipal Mosgortrans bus lines connect to Moscow.
• The Leningradskoye Highway is the primary route that leads to the airport.

Moscow Domodedovo Airport

Moscow Domodedovo International Airport is an International Airpor located in Domodedovo, Moscow Oblast. It serves Moscow. After Sheremetyevo, it is the second-biggest airport in Russia. It is among the twenty busiest airports in Europe.

• The airport handled 21.2 million passengers in 2022.
• The airport is named after the town of Domodedovo.
• Dmitry Kamenshchik is the owner of the airport.
• The airport was opened on 7 April 1962.
• This airport is a hub for Red Wings Airlines, S7 Airlines, and Ural Airlines.
• As part of the 2018 FIFA World Cup plans, the airport’s Terminal 2 initial phase was constructed for international flights.
• There is a train station at the airport that connects to Moscow’s central Paveletsky Rail Terminal.
• The terminal itself is reachable from the junction of Moscow Ring Road and Kashirskoye Highway.

Vnukovo International Airport

Vnukovo International Airport is formally known as Vnukovo Andrei Tupolev International Airport. It is situated in Moscow’s Vnukovo District.

• It is named after Andrei Tupolev.
• This international airport has two runways.
• The airport served 24.01 million travelers in 2019.
• In 2021, Vnukovo ranked as the ninth busiest airport in Europe.
• Azimuth, Azur Air, Gazpromavia, I-Fly, Pobeda, RusLine, and Utair all use it as a hub.
• The elevation of Vnukovo is 204 meters above sea level.
• The airport features 60 aircraft stands, one general aviation terminal (for charter and corporate flights), two passenger terminals (Terminal A and Terminal B), and one cargo terminal.
• The Aeroexpress direct line links the Kiyevsky Rail Terminal in the center of Moscow with Vnukovo Airport.
• Bus lines operated by the municipal Mosgortrans provide access to Moscow city.
• The airport offers a number of taxi services to the city and suburbs of Moscow. Yandex, Gett, Uber.
• The Moscow Metro Line 8a terminus, “Aeroport Vnukovo” Metro station, serves it.

Sochi International Airport

Sochi International Airport is an international airport on the Black Sea coast, situated in the Adler District of the resort city of Sochi.

• It opened as an airport on November 23, 1945.
• An international joint venture between Basic Element group, Sberbank, and Changi Airports International manages the airport.
• The airport is owned by Oleg Deripaska.
• Two man-made runways can be found at Sochi International Airport.
• Next to the terminal and connected by a walkway bridge is the Sochi Airport train station.

Simferopol International Airport

Simferopol International Airport is a public airport located in Simferopol, Crimea. Simferopol International Airport serves the city of Simferopol in the Crimean peninsula.

• It was constructed in 1936. There is one domestic terminal and one international terminal at the airport.
• Due to the lack of international recognition of the Russian Federation’s takeover of Crimea in 2014, the airport is only utilized for flights to and from Russian airports.
• 6.83 million passengers passed through this airport in 2022.
• From the airport, a trolleybus travels to the Simferopol Railway station.
• There is a direct bus service from the airport to Sevastopol Bus Station.

Ufa International Airport

Ufa International Airport is the major airport serving Ufa, the capital of Bashkortostan in Russia. The airport served 2,810,000 passengers in 2017.

• It’s Russia’s tenth-biggest airport.
• It serves as a hub for carriers such as Azur Air and Ural Airlines.
• It is elevated 137 meters above sea level.
• The airport and the city of Ufa are connected by the city bus routes.

Platov International Airport

Platov International Airport is located in Aksaysky District, Rostov Oblast, Russia. Rostov-on-Don is served by it.

• It opened for business in December 2017.
• It bears Matvei Platov’s name.
• It serves as Azimuth Airlines’ hub.
• It is elevated 213 feet above sea level.
• The airport terminal is 50,000 square meters in size.
• Three million travelers passed through it in 2019.
• The M4 highway connects the airport to the city center.

Kurumoch International Airport

Kurumoch International Airport is an international airport that serves Samara, Tolyatti.

• On May 15, 1961, Kurumoch opened for business as a domestic airport.
• Its elevation from sea level is 145 meters.
• In 2017, it handled 2,649,426 in traffic.
• The majority of domestic flights in Russia are handled at Terminal 1. International flights are handled only by Terminal 2.

Kazan International Airport

Kazan International Airport is an international airport in Russia. It may be found in the Kazan city.

• In Tatarstan, it is the biggest airport.
• Airport underwent a name change in 2019 to honor a Volga Tatar.
• The completion date of Kazan 2 was September 15, 1979.
• It benefits Russia and Kazan.
• It serves as UVT Aero’s hub.
• It is elevated 125 meters above sea level.
• In 2023, it provided service to over 5 million passengers.

Vladivostok International Airport

Vladivostok International Airport is a major international airport at Artyom in Primorsky Krai, Russia. Previously, it was called Knevichi Airport.

• In 1931, the Vladivostok Airport was built.
• It is elevated 14 meters above sea level.
• It serves as Aurora, S7 Airlines’ hub.
• The airport has two passenger terminals: the new International Terminal A and the outdated Domestic Terminal B. There are two nearby airfields: Knevichi and Lake Springs.
• In 2023, it provided service to 2.5 million passengers.

Read More :

• List of International Airports in India
• List of Airports in the UAE
• List of Airports in Singapore
• International Airlines of All Major Countries
• List of International Airports in Kerala in 2024
• 10 Largest Airports in the World in 2024

FAQs on List of International Airports in Russia

How many international airports does russia have.

Russia has over 50 are international airports.

What is the largest international airport in Russia?

Sheremetyevo International Airport is the largest international airport in Russia.

What is Russia’s main international airport?

Sheremetyevo International Airport is Russia’s main international airport.

What is the busiest international airport in Russia?

Sheremetyevo International Airport is the busiest international airport in Russia.

Which airport is bigger in Moscow?

Domodedovo Airport is biggest airport in Moscow.

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