**NPTEL 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. To meet this requirement object-oriented paradigm has been developed and based on this paradigm the Introduction to Machine Learning emerges as the best programming environment.

**Introduction to Machine Learning is a MOOC based course that** is 12 weeks in duration and can fulfill the criteria of 4 credits in a year. You can visit the NPTEL SWAYAM platform and register yourself for the course. This course is brought to you by

**Prof. Balaraman Ravindran**is currently a Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow.

## Introduction To Machine Learning 2021 Details:-

Contents

**Who Can Join:**This is an elective course. Intended for senior UG/PG students. BE/ME/MS/PhD**Requirements/Prerequisites:**We will assume that the students know programming for some of the assignments.If the students have done introductory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.**INDUSTRY SUPPORT:**Any company in the data analytics/data science/big data domain would value this course.

### Introduction To Machine Learning Assignment Week 12 Answers:-

**Q1.**Which of the following measure best analyze the performance of a classifier?

**Answer:-** **D – Depends on the application**

**FOR JULY 2021 ANSWERS VISIT :- INTRODUCTION TO MACHINE LEARNING ASSIGNMENT ANSWERS JULY 2021**

**Q2.** As discussed in the lecture, most of the classifiers minimize the empirical risk. Which among the following is an exceptional case?

**Answer:-** **B- Support Vector Machines**

**Q3.** What do you expect to happen to the variance component of the generalisation error of your model as the size of the training data set increases?

**Answer:-** **C- Decrease in variance**

**Q4.** After completing *Introduction to Machine Learning *on NPTEL, you have landed a job as a Data Scientist at YumEll Solutions Inc. Your first assignment as a trainee is to learn a classifier given some data and present insights on it to your manager, who apparently doesn’t seem to have any knowledge on Machine Learning. Which of the following classification models would you pick to best explain the nature of the data and the underlying distribution to your manager?

**Answer:-** **A- Linear Models**

**Q5.** What happens when your model complexity (such as interaction terms in linear regression, order of polynomial in SVM, etc.) increases?

**Answer:-** **B,c **

**Q6.** Suppose we want an RL agent to learn to play the game of golf. For training purposes, we make use of a golf simulator program. Assume that the original reward distribution gives a reward of +10 when the golf ball is hit into the hole and -1 for all other transitions. To aid the agent’s learning process, we propose to give an additional reward of +3 whenever the ball is within a 1 metre radius of the hole. Is this additional reward a good idea or not? Why?

**Answer:-** **C- No. The additional reward may actually hinder learning.**

**Q7.** You want to toss a fair coin a number of times and obtain the probability of getting heads by taking a simple average. What is the estimated number of times you’ll have to toss the coin to make sure that your estimated probability is within 10% of the actual probability, at least 90% of the time?

**Answer:-** **C- 200*ln(20)**

**Q8.** A new phone, E-Corp X1 has been announced and it is what you’ve been waiting for, all along. You decide to read the reviews before buying it. From past experiences, you’ve figured out that good reviews mean that the product is good 90% of the time and bad reviews mean that it is bad 70% of the time. Upon glancing through the reviews section, you find out that the X1 has been reviewed 1269 times and only 127 of them were bad reviews. What is the probability that, if you order the X1, it is a bad phone?

**Answer:-** **C – 0.160**

**Q9.** You face a particularly challenging RL problem, where the reward distribution keeps changing with time. In order to gain maximum reward in this scenario, does it make sense to stop exploration or continue exploration?

**Answer:- Continue exploration**

### Introduction To Machine Learning Assignment Week 11 Answers:-

**Q1.** Given *n* samples *x*1,*x*2,…,*xN* drawn independently from an Exponential distribution unknown parameter *λ*, find the MLE of *λ*.

**Answer:-** **A – λMLE=∑ni=1xi**

**Q2.** Given *n* samples *x*1,*x*2,…,*xn* drawn independently from an Geometric distribution unknown parameter *p* given by pdf *Pr*(*X*=*k*)=(1−*p*)*k*−1*p* for *k*=1,2,3,⋅⋅⋅ , find the MLE of *p*.

**Answer:-** **C – pMLE=n∑ni=1xi**

**Q3.** Suppose we are trying to model a *p* dimensional Gaussian distribution. What is the actual number of independent parameters that need to be estimated in mean and covariance matrix respectively?

**Answer:-** **E – p,p(p+1)**

**Q4.** Given *n* samples *x*1,*x*2,…,*xN* drawn independently from a Poisson distribution unknown parameter *λ*, find the MLE of *λ*.

**Answer:-** **F – λMLE=∑ni=1xin−1**

**Q5.** In Gaussian Mixture Models, *πi* are the mixing coefficients. Select the correct conditions that the mixing coefficients need to satisfy for a valid GMM model.

**Answer:-** **B,C**

**Q6.** Expectation-Maximization, or the EM algorithm, consists of two steps – E step and the M-step. Using the following notation, select the correct set of equations used at each step of the algorithm.

Notation.

**Answer:-** **B,D**

### Introduction To Machine Learning Assignment Week 10 Answers:-

**Q1.** Consider the following one dimensional data set: 12, 22, 2, 3, 33, 27, 5, 16, 6, 31, 20, 37, 8 and 18. Given *k*=3 and initial cluster centers to be 5, 6 and 31, what are the final cluster centres obtained on applying the k-means algorithm?

**Answer:-** **D – 4.8, 17.6, 32**

**Q2.** For the previous question, in how many iterations will the k-means algorithm converge?

**Answer:-** **C – 4**

**Q3.** In the lecture on the BIRCH algorithm, it is stated that using the number of points N, sum of points **SUM** and sum of squared points **SS**, we can determine the centroid and radius of the combination of any two clusters A and B. How do you determine the centroid of the combined cluster? (In terms of **N,SUM** and **SS** of both the clusters)

**Answer:-** **D – SSA+SSB/NA+NB**

**Q4.** What assumption does the CURE clustering algorithm make with regards to the shape of the clusters?

**Answer:-** **A – No assumption**

**Q5.** What would be the effect of increasing MinPts in DBSCAN while retaining the same Eps parameter? (Note that more than one statement may be correct)

**Answer:-** **B,C**

For the next question, kindly download the dataset – DS1. The first two columns in the dataset correspond to the co-ordinates of each data point. The third column corresponds two the actual cluster label.**DS1: https://bit.ly/2Lm75Ly**

**Q6**. Visualize the dataset DS1. Which of the following algorithms will be able to recover the true clusters (first check by visual inspection and then write code to see if the result matches to what you expected).

**Answer:-** **C – Single link hierarchical clustering**

**Q7.** Consider the similarity matrix given below: Which of the following shows the hierarchy of clusters created by the single link clustering algorithm.

**Answer:-** **B **

**Q8.** For the similarity matrix given in the previous question, which of the following shows the hierarchy of clusters created by the complete link clustering algorithm.

**Answer:-** **D **

### Introduction To Machine Learning Assignment Week 9 Answers:-

**Q1.** Consider the bayesian network shown below.

Two students – Manish and Trisha make the following claims:

• Manish claims *P*(*D*|{*S*,*L*,*C*})=*P*(*D*|{*L*,*C*})• Trisha claims *P*(*D*|{*S*,*L*})=*P*(*D*|*L*)

where *P*(*X*|*Y*) denotes probability of event *X* given *Y* . Please note that Y can be a set. Which of the following is true?

**Answer:-** **B – Manish is correct and Trisha is incorrect.**

**Q2.** Consider the same bayesian network shown in previous question (Figure 1). Two other students in the class – Trina and Manish make the following claims:

• Trina claims P(*S*|{*G*,*C*})=*P*(*S*|*C*)

• Manish claims *P*(*L*|{*D*,*G*})=*P*(*L*|*G*)

Which of the following is true?

**Answer:-** **B – Trina is incorrect and Manish is correct.**

**Q3.**Consider the Bayesian graph shown below in Figure 2.

The random variables have the following notation: d – Difficulty, i – Intelligence, g – Grade, s -SAT, l – Letter. The random variables are modeled as discrete variables and the corresponding CPDs are as below.

What is the probability of *P*(*i*=1,*d*=0,*g*=2,*s*=1,*l*=1)

**Answer:-** **E – 0.009216**

**Q4.** Using the data given in the previous question, compute the probability of following assignment, *P*(*i*=1,*g*=1,*s*=1,*l*=0) irrespective of the difficulty of the course? (up to 3 decimal places)

**Answer:-** ** D- **0.047

**Q5.** Consider the Bayesian network shown below in Figure 3

Two students – Manish and Trisha make the following claims:

• Manish claims *P*(*H*|{*S*,*G*,*J*})=*P*(*H*|{*G*,*J*})• Trisha claims *P*(*H*|{*S*,*C*,*J*})=*P*(*H*|{*C*,*J*})

Which of the following is true?

**Answer:-** **D – Manish is correct and Trisha is incorrect.**

**Q6.** Consider the Markov network shown below in Figure 4

Which of the following variables are NOT in the markov blanket of variable “4” shown in the above Figure 4 ? (multiple answers may be correct)

**Answer:-B,C,D,G**

**Q7.** In the Markov network given in Figure 4, two students make the following claims:

• Manish claims variable “1” is dependent on variable “7” given variable “2”.• Trina claims variable “2” is independent of variable “6” given variable “3”.

Which of the following is true?

**Answer:-** **B- Trina is incorrect and Manish is correct.**

**Q8.** Four random variables are known to follow the given factorization

*P*(*A*1=*a*1,*A*2=*a*2,*A*3=*a*3,*A*4=*a*4)=1*Z**ψ*1(*a*1,*a*2)*ψ*2(*a*1,*a*4)*ψ*3(*a*1,*a*3)*ψ*4(*a*2,*a*4)*ψ*5(*a*3,*a*4)

The corresponding Markov network would be

**Answer:-** **E**

**Q9.** Does there exist a more compact factorization involving less number of factors for the distribution given in previous question?

**Answer:-** **A – YES**

**Q10.** Consider the following Markov Random Field.

Which of the following nodes will have no effect on H given the Markov Blanket of H?

**Answer:-** **A,I,J**

### Introduction To Machine Learning Assignment Week 8 Answers:-

**Q1.** The Naive Bayes classifier makes the assumption that the **__are independent given the ___**.

**Answer:-** **A – features, class labels**

**Q2.** Can the decision boundary produced by the Naive Bayes algorithm be non-linear?

**Answer:-** **B – yes**

**Q3.** A major problem of using the one vs. rest multi-class classification approach is:

**Answer:-** **A – class imbalance**

**Q4.** Consider the problem of learning a function *X*→*Y* , where *Y* is Boolean. *X* is an input vector (*X*1,*X*2), where *X*1 is categorical and takes 3 values, and *X*2 is a continuous variable (normally distributed). What would be the minimum number of parameters required to define a Naive Bayes model for this function?

**Answer:-** ** A – 8**

**Q5.** In boosting, the weights of data points that were miscalssified are ** __** as training progresses.

**Answer:-** ** B – increased**

**Q6.** In a random forest model let *m*<<*p* be the number of randomly selected features that are used to identify the best split at any node of a tree. Which of the following are true? (*p* is the original number of features)

(Multiple options may be correct)

**Answer:-** **B,C**

**Q7.** Consider the following graphical model, which of the following are false about the model? (multiple options may be correct)

**Answer:-** **A,D**

**Q8.** Consider the Bayesian network given in the previous question. Let ‘A’, ‘B’, ‘C’, ‘D’and ‘E’denote the random variables shown in the network. Which of the following can be inferred from the network structure?

**Answer:-** **E – none of the above can be inferred**

### Introduction To Machine Learning Assignment Week 7 Answers:-

**Q1.** For the given confusion matrix, compute the recall

Answer:-D - 0.67

**Q2.** Pallavi is working on developing a binary classifier which has a huge class imbalance. Which of the following metric should she optimize the classifier over to develop a good model?

**Answer:-** **D – F-Score**

**Q3.** For large datasets, we should always be choosing large *k* while doing *k*− fold cross validation to get better performance on test set.

**Answer:-** **False**

**Q4.** While designing an experiment, which of these aspects should be considered?

**Answer:-** **A,B,C**

**Q5.** Which of the following are true?

TP – True Positive, TN – True Negative, FP – False Positive, FN – False Negative

**Answer:-** **A,B**

**Q6.** In the ROC plot, what are the quantities along x and y axes respectively?

**Answer:-** ** C – True Positive Rate, False Positive Rate**

**Q7.** How does bagging help in improving the classification performance?

**Answer:-** **B,C**

**Q8.** Which method among bagging and stacking should be chosen in case of limited training data? and What is the appropriate reason for your preference?

**Answer:-** **D **

**Q9.** Which of the following statements are false when comparing Committee Machines and Stacking

**Answer:-** **B,C**

### Introduction To Machine Learning Assignment Week 6 Answers:-

**Q1.** When building models using decision trees we essentially split the entire input space using

**Answer:-** **C – polynomial curves of the same order as the length of decision tree**

**Q2.** In building a decision tree model, to control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true?

**Answer:-** **A,B**

**Q3.** Suppose we use the decision tree model for solving a multi-class classification problem. As we continue building the tree, w.r.t. the generalisation error of the model,

**Answer:-** **B,C**

**Q4.** Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. The average value of the outputs of data points denotes the response of a branch. The original responses for data points 1along the two branches (left right respectively) were *response _left* and,* response_right* and the new response after collapsing the node is *response_new*. What are the values for *response_left*, *response_right* and *response_new* (numbers in the option are given in the same order)?

**Answer:-** **B – 7.2; 10; 8.8**

**Q5.** Consider the following dataset: Which among the following split-points for the* feature _{1}* would give the best split according to the information gain measure?

**Answer:-** **16.85**

**Q6.** For the same dataset, which among the following split-points for *feature2 *would give the best split according to the gini index measure?

**Answer:-** **176.35**

**Q7.** In which of the following situations is it appropriate to introduce a new category ’Missing’ for missing values? (multiple options may be correct)

**Answer:-** **A,D**

### Introduction To Machine Learning Assignment Week 5 Answers:-

**Q1.** You are given the N samples of input (x) and output (y) as shown in the figure below. What will be the most appropriate model *y*=*f*(*x*)?

**Answer:-** **C – y=x^w with w>0**

**Q2.** For training a binary classification model with five independent variables, you choose to use neural networks. You apply one hidden layer with three neurons. What are the number of parameters to be estimated? (Consider the bias term as a parameter)

**Answer:-** **F – 22**

**Q3.** Suppose the marks obtained by randomly sampled students follow a normal distribution with unknown μ. A random sample of 5 marks are 25, 55, 64, 7 and 99. Using the given samples find the maximum likelihood estimate for the mean.

**Answer:-** **C – 50**

**Q4.** You are given the following neural networks which take two binary valued inputs *x*1,*x*2∈{0,1} and the activation function is the threshold function(*h*(*x*)=1 if *x*>0;0 otherwise). Which of the following logical functions does it compute?

**Answer:-** **A – OR**

**Q5. **Using the notations used in class, evaluate the value of the neural network with a 3-3-1 archi- tecture (2-dimensional input with 1 node for the bias term in both the layers). The parameters are as follows

*α*=[1−10.20.80.40.5]

*β*=[0.80.40.5]

Using sigmoid function as the activation functions at both the layers, the output of the network for an input of (0.8, 0.7) will be

**Answer:-** **F – 0.7977**

**Q6. **Which of the following statements are true:

**Answer:-** **Multiple choice** B,C

**Q7. **We have a function which takes a two-dimensional input *x*=(*x*1,*x*2) and has two parameters *w*=(*w*1,*w*2) given by *f*(*x*,*w*)=*σ*(*σ*(*x*1*w*1)*w*2+*x*2) where *σ*(*x*)=11+*e*−*x* We use backprop- agation to estimate the right parameter values. We start by setting both the parameters to 1. Assume that we are given a training point *x*2=1,*x*1=0,*y*=5. Given this information answer the next two questions. What is the value of ∂*f*∂*w*2?

**Answer:- ** **D – 0.098**

**Q8. **If the learning rate is 0.5, what will be the value of *w*2 after one update using backpropagation algorithm?

**Answer:-** **A – 0.4197**

**Q9.** Which of the following are true when comparing ANNs and SVMs?

**Answer:- Multiple choice** – **A,B,D**

**10. **Which of the following are correct?

**Answer:- Multiple Choice** – **A,C**

### Introduction To Machine Learning Assignment Week 4 Answers:-

**Q1.** Consider a Boolean function in three variables, that returns True if two or more variables out of three are True, and False otherwise. Can this function be implemented using the perceptron algorithm?

**Answer:-** **B – Yes**

**Q2.** For a support vector machine model, let *xi* be an input instance with label *yi* . If *yi*(*β*^0+*xTiβ*^)>1, where *β*0 and *β*^) are the estimated parameters of the model, then

**Answer:- ** **A – xi is not a support vector**

**Q3.** Suppose we use a linear kernel SVM to build a classifier for a 2-class problem where the training data points are linearly separable. In general, will the classifier trained in this manner be always the same as the classifier trained using the perceptron training algorithm on the same training data?

**Answer:-** **B – No**

**Introduction To Machine learning:-Note these Answers are based on our knowledge **

**Q4.** Train a linear regression model (without regularization) on the above dataset. Report the coefficients of the best fit model. Report the coefficients in the following format: *β*0,*β*1,*β*2,*β*3 . (You can round-off the accuracy value to the nearest 2-decimal point number.)

**Answer:-** **D – 1, -1.2, 2.1, 2.2**

**Q5.** Train an l2 regularized linear regression model on the above dataset. Vary the regularization parameter from 1 to 10. As you increase the regularization parameter, absolute value of the coefficients (excluding the intercept) of the model:

**Answer:-** **C – decrease**

**Q6.** Train an *l*2 regularized logistic regression classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Kindly note that the *C* parameter mentioned below is the inverse of the regularization parameter *λ*. As part of the assignment train a model with the following hyperparameters:

Model: logistic regression with one-vs-rest classifier, *C*=1*e*4

For the above set of hyperparameters, report the best classification accuracy

**Answer:-** **B – 0.86**

**Q7.** Train an SVM classifier on the modified iris dataset. We recommend using sklearn. Use only the first two features for your model. We encourage you to explore the impact of varying different hyperparameters of the model. Specifically try different kernels and the associated hyperparameters. As part of the assignment train models with the following set of hyperparameters

RBF-kernel, *gamma*=0.5, one-vs-rest classifier, no-feature-normalization. Try *C*=0.01,1,10. For the above set of hyperparameters, report the best classification accuracy along with total number of support vectors on the test data.

**Answer:-** **C – 0.88, 69**

### Machine Learning Assignment Week 3 Answers:-

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

**Answer:- ** **B,C**

**Q2.** 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?

**Answer:- C**

**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?

**Answer:-** **E**

**Q4.** Given the following distribution of data points: What method would you choose to perform Dimensionality Reduction?

**Answer:-** **A**

**Disclaimer:- Note these answers are based on our know knowledge,so kindly verify. **

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

**Answer:-** **C**

##### JOIN US ON TELEGRAM

**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*+*X*2+*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*?

**Answer:-** **B**

**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

**Answer:-** **A**

**Q8.** Consider the following data: Assuming that you apply LDA to this data, what is the estimated covariance matrix?

**Answer:-** **C**

**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)

**Answer:-** **D**

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

**Answer:-** **B**

### Machine Learning Assignment Week 2 Answers:-

**Q1. **Given a training data set of 10,000 instances, with each input instance having 17 dimensions and each output instance having 2 dimensions, the dimensions of the design matrix used in applying linear regression to this data is

**Answer:- C**

**FOR JULY 2021 ANSWERS VISIT :- INTRODUCTION TO MACHINE LEARNING ASSIGNMENT ANSWERS JULY 2021**

**Q2. **Suppose we want to add a regularizer to the linear regression loss function, to control the magnitudes of the weights *β*. We have a choice between Ω1(*β*)=Σ*pi*=1|*β*| and Ω2(*β*)=Σ*pi*=1*β*2 Which one is more likely to result in sparse weights?

**Answer:- A**

**Q3. **The model obtained by applying linear regression on the identified subset of features may differ from the model obtained at the end of the process of identifying the subset during

**Answer:-** **C**

**Q4. **Consider forward selection, backward selection and best subset selection with respect to the same data set. Which of the following is true?

**Answer:- A**

**FOR JULY 2021 ANSWERS VISIT :- INTRODUCTION TO MACHINE LEARNING ASSIGNMENT ANSWERS JULY 2021**

**Q5.** In the lecture on Multivariate Regression, you learn about using orthogonalization iteratively to obtain regression co-effecients. This method is generally referred to as Multiple Regression using Successive Orthogonalization. In the formulation of the method, we observe that in iteration *k*, we regress the entire dataset on *z*0,*z*1,…*zk*−1 . It seems like a waste of computation to recompute the coefficients for *z*0 a total of *p* times, *z*1 a total of *p*−1 times and so on. Can we re-use the coefficients computed in iteration *j* for iteration *j*+1 for *zj*−1 ?

**Answer:-** **B**

**Q6. **Principal Component Regression (PCR) is an approach to find an orthogonal set of basis vectors which can then be used to reduce the dimension of the input. Which of the following matrices contains the principal component directions as its columns

**Answer:- ** **D- V**

**Q7. **Consider the following five training examples , We want to learn a function *f*(*x*) of the form *f*(*x*)=*ax*+*b* which is parameterised by (*a*,*b*). Using squared error as the loss function, which of the following parameters would you use to model this function to get a solution with the minimum loss.

**Answer:- C**

**Q8. **Here is a data set of words in two languages.

Let us build a nearest neighbours classifier that will predict which language a word belongs to. Say we represent each word using the following features.• Length of the word• Number of consonants in the word• Whether it ends with the letter ’o’ (1 if it does, 0 if it doesn’t)

For example, the representation of the word ‘waffle’ would be [6, 2, 0]. For a distance function, use the Manhattan distance.

*d*(*a*,*b*)=Σ*n**i*=1|*a**i*−*b**i*| where *a*,*b*∈*R**n*

Take the input word ‘keto’. With k = 1, the predicted language for the word is?

**Answer:-**** B**

### ML Quiz Assignment Week 1 Answers:-

**Q1**. Which of the following is a supervised learning problem?

**Answer/Code:-** **CLICK HERE FOR JULY 2021 ANSWERS**

** Q2 – **Which of the following is not a classification problem?

**Answer/Code:-** **CLICK HERE FOR JULY 2021 ANSWERS**

**FOR JULY 2021 INTRODUCTION TO MACHINE LEARNING ANSWERS VISIT OUR TELEGRAM OR WHATSAPP GROUP**

**FOR JULY 2021 ANSWERS VISIT :- INTRODUCTION TO MACHINE LEARNING ASSIGNMENT ANSWERS JULY 2021**

**Q3 – **Which of the following is a regression task? (multiple options may be correct)

**Answer:-** **CLICK HERE FOR JULY 2021 ANSWERS**

**Q4 – **Which of the following is an unsupervised task?

**Answer:- CLICK HERE FOR JULY 2021 ANSWERS**

**FOR JULY 2021 ANSWERS VISIT :- INTRODUCTION TO MACHINE LEARNING ASSIGNMENT ANSWERS JULY 2021**

**FOR JULY 2021 INTRODUCTION TO MACHINE LEARNING ANSWERS VISIT OUR TELEGRAM OR WHATSAPP GROUP**

**Q5 – Which of the following is a categorical feature?**

**Answer:-** **CLICK HERE FOR JULY 2021 ANSWERS**

**Q6 – **Let *X* and *Y* be a uniformly distributed random variable over the interval [0, 4] and [0, 6] respectively. If *X* and *Y* are independent events, then compute the probability, P(max(*X*,*Y*)>3)

**Answer:- CLICK HERE FOR JULY 2021 ANSWERS**

**FOR JULY 2021 INTRODUCTION TO MACHINE LEARNING ANSWERS VISIT OUR TELEGRAM OR WHATSAPP GROUP**

**Q7 – **Let the trace and determinant of a matrix *A*[*acbd*] be 6 and 16 respectively. The eigenvalues of A are.

**Answer:- CLICK HERE FOR JULY 2021 ANSWERS**

**Q8 – **What happens when your model complexity increases? (multiple options may be correct)

**Answer:- CLICK HERE FOR JULY 2021 ANSWERS **

**FOR JULY 2021 ANSWERS VISIT :- INTRODUCTION TO MACHINE LEARNING ASSIGNMENT ANSWERS JULY 2021**

**Q9 – **A new phone, E-Corp X1 has been announced and it is what you’ve been waiting for, all along. You decide to read the reviews before buying it. From past experiences, you’ve figured out that good reviews mean that the product is good 90% of the time and bad reviews mean that it is bad 70% of the time. Upon glancing through the reviews section, you find out that the X1 has been reviewed 1269 times and only 172 of them were bad reviews. What is the probability that, if you order the X1, it is a bad phone?

**Answer:- ****CLICK HERE FOR JULY 2021 ANSWERS**

**FOR JULY 2021 INTRODUCTION TO MACHINE LEARNING ANSWERS VISIT OUR TELEGRAM OR WHATSAPP GROUP**

**Q10 – **Which of the following are false about bias and variance of overfitted and underfitted models? (multiple options may be correct)

**Answer:- CLICK HERE FOR JULY 2021 ANSWERS**

#### Find Other Quiz Here**:**

NPTEL » Programming in C++ Assignment week 01 2021

Amazon Fashion Quiz Answers & Win ₹1000

Amazon Great Indian Festival Quiz Answers: Win Rs. 50,000

NPTEL » Programming In Java Assignment week 6 Sep-2020

NPTEL » Programming In Java Assignment week 7 Sep-2020

**NOTE: These codes are** **based on our knowledge**.** **A**nswers might be incorrect**, **we suggest you to not the copy-paste answers blindly.**

[foobar id=”1315″]