Hello Peers, Today we will share all week’s assessment and quiz answers of the Machine Learning: Clustering & Retrieval course launched by Coursera free of cost✅✅✅. This is a certification course for every interested student.
In case you didn’t find this course for free, then you can apply for financial ads to get this course for totally free.
Check out this article – “How to Apply for Financial Ads?”
- About The Coursera
- About Machine Learning: Clustering & Retrieval Course
- Machine Learning: Clustering & Retrieval Quiz Answers
- Quiz 1: Representations and metrics
- Quiz 2: Choosing features and metrics for nearest neighbor search
- Quiz 3: KD-trees
- Quiz 4: Locality Sensitive Hashing
- Quiz 5: Implementing Locality Sensitive Hashing from scratch
- Week 3: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
- Quiz 1: k-means
- Quiz 2: Clustering text data with K-means
- Quiz 3: MapReduce for k-means
- Week 4: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
- Quiz 1: EM for Gaussian mixtures
- Quiz 2: Implementing EM for Gaussian mixtures
- Quiz 3: Clustering text data with Gaussian mixtures
- Week 5: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
- Quiz 1: Latent Dirichlet Allocation
- Quiz 2: Learning LDA model via Gibbs sampling
- Quiz 3: Modeling text topics with Latent Dirichlet Allocation
- Week 6: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
- Quiz 1: Modeling text data with a hierarchy of clusters
- More About This Course
About The Coursera
Coursera, India’s biggest learning platform launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach in a very well manner and in a more understandable way.
Here, you will find Machine Learning: Clustering & Retrieval Exam Answers in Bold Color which are given below.
These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Machine Learning: Clustering & Retrieval from Coursera Free Certification Course.
Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.
About Machine Learning: Clustering & Retrieval Course
In this third case study, locating related documents, you will investigate similarity-based retrieval techniques. This course will also look at structured representations for representing documents in a corpus, such as clustering and mixed membership models like latent Dirichlet allocation (LDA). You will use expectation maximization (EM) to discover how to cluster documents and scale the approaches using MapReduce.
Course Apply Link – Machine Learning: Clustering & Retrieval
Machine Learning: Clustering & Retrieval Quiz Answers
Quiz 1: Representations and metrics
Question 1: Consider three data points with two features as follows:
Among the three points, which two are closest to each other in terms of having the smallest Euclidean distance?
- A and B
- A and C
- B and C
Question 2: Consider three data points with two features as follows:
Among the three points, which two are closest to each other in terms of having the largest cosine similarity (or equivalently, smallest cosine distance)?
- A and B
- A and C
- B and C
Question 3: Consider the following two sentences.
- Sentence 1: The quick brown fox jumps over the lazy dog.
- Sentence 2: A quick brown dog outpaces a quick fox.
Compute the Euclidean distance using word counts. To compute word counts, turn all words into lower case and strip all punctuation, so that “The” and “the” are counted as the same token. That is, document 1 would be represented as
x=[# the,# a,# quick,# brown,# fox,# jumps,# over,# lazy,# dog,# outpaces]
where # word is the count of that word in the document.
Round your answer to 3 decimal places.
Question 4: Consider the following two sentences.
- Sentence 1: The quick brown fox jumps over the lazy dog.
- Sentence 2: A quick brown dog outpaces a quick fox.
Recall that
cosine distance = 1 – cosine similarity = 1- \frac{x^T y}{||x|| ||y||}1−∣∣x∣∣∣∣y∣∣xTy
Compute the cosine distance between sentence 1 and sentence 2 using word counts. To compute word counts, turn all words into lower case and strip all punctuation, so that “The” and “the” are counted as the same token. That is, document 1 would be represented as
x=[# the,# a,# quick,# brown,# fox,# jumps,# over,# lazy,# dog,# outpaces]
where # word is the count of that word in the document.
Round your answer to 3 decimal places.
Answer:
Question 5: (True/False) For positive features, cosine similarity is always between 0 and 1.
- True
- False
Question 6: Which of the following does not describe the word count document representation? (Note: this is different from TF-IDF document representation.)
- Ignores the order of the words
- Assigns a high score to a frequently occurring word
- Penalizes words that appear in every document
Quiz 2: Choosing features and metrics for nearest neighbor search
Question 1: Among the words that appear in both Barack Obama and Francisco Barrio, take the 5 that appear most frequently in Obama. How many of the articles in the Wikipedia dataset contain all of those 5 words?
Answer: 56066
Question 2: Measure the pairwise distance between the Wikipedia pages of Barack Obama, George W. Bush, and Joe Biden. Which of the three pairs has the smallest distance?
- Between Obama and Biden
- Between Obama and Bush
- Between Biden and Bush
Question 3: Collect all words that appear both in Barack Obama and George W. Bush pages. Out of those words, find the 10 words that show up most often in Obama’s page. Which of the following is NOT one of the 10 words?
- the
- presidential
- in
- act
- his
Question 4: Among the words that appear in both Barack Obama and Phil Schiliro, take the 5 that have largest weights in Obama. How many of the articles in the Wikipedia dataset contain all of those 5 words?
Answer: 14
Question 5: Compute the Euclidean distance between TF-IDF features of Obama and Biden. Round your answer to 3 decimal places. Use American-style decimals (e.g. 110.921).
Answer: 123.298
Quiz 3: KD-trees
Question 1: Which of the following is not true about KD-trees?
- It divides the feature space into nested axis-aligned boxes.
- It can be used only for approximate nearest neighbor search but not for exact nearest neighbor search.
- It prunes parts of the feature space away from consideration by inspecting smallest possible distances that can be achieved.
- The query time scales sublinearly with the number of data points and exponentially with the number of dimensions.
- It works best in low to medium-dimension settings.
Question 2: Questions 2, 3, 4, and 5 involve training a KD-tree on the following dataset:
X1 | X2 | |
Data point 1 | -1.58 | -2.01 |
Data point 2 | 0.91 | 3.98 |
Data point 3 | -0.73 | 4.00 |
Data point 4 | -4.22 | 1.16 |
Data point 5 | 4.19 | -2.02 |
Data point 6 | -0.33 | 2.15 |
Train a KD-tree by hand as follows:
- First split using X1 and then using X2. Alternate between X1 and X2 in order.
- Use “middle-of-the-range” heuristic for each split. Take the maximum and minimum of the coordinates of the member points.
- Keep subdividing until every leaf node contains two or fewer data points.
What is the split value used for the first split? Enter the exact value, as you are expected to obtain a finite number of decimals. Use American-style decimals (e.g. 0.026).
Question 3: Refer to Question 2 for context.
What is the split value used for the second split? Enter the exact value, as you are expected to obtain a finite number of decimals. Use American-style decimals (e.g. 0.026).
Question 4: Refer to Question 2 for context.
Given a query point (-3, 1.5), which of the data points belong to the same leaf node as the query point? Choose all that apply.
- Data point 1
- Data point 2
- Data point 3
- Data point 4
- Data point 5
- Data point 6
Question 5: Refer to Question 2 for context.
Perform backtracking with the query point (-3, 1.5) to perform exact nearest neighbor search. Which of the data points would be pruned from the search? Choose all that apply.
Hint: Assume that each node in the KD-tree remembers the tight bound on the coordinates of its member points, as follows:
- Data point 1
- Data point 2
- Data point 3
- Data point 4
- Data point 5
- Data point 6
Quiz 4: Locality Sensitive Hashing
Question 1: (True/False) Like KD-trees, Locality Sensitive Hashing lets us compute exact nearest neighbors while inspecting only a fraction of the data points in the training set.
- True
- False
Question 2: (True/False) Given two data points with high cosine similarity, the probability that a randomly drawn line would separate the two points is small.
- True
- False
Question 3: (True/False) The true nearest neighbor of the query is guaranteed to fall into the same bin as the query.
- True
- False
Question 4: (True/False) Locality Sensitive Hashing is more efficient than KD-trees in high dimensional setting.
- True
- False
Question 5: Suppose you trained an LSH model and performed a lookup using the bin index of the query. You notice that the list of candidates returned are not at all similar to the query item. Which of the following changes would not produce a more relevant list of candidates?
- Use multiple tables.
- Increase the number of random lines/hyperplanes.
- Inspect more neighboring bins to the bin containing the query.
- Decrease the number of random lines/hyperplanes.
Quiz 5: Implementing Locality Sensitive Hashing from scratch
Question 1: What is the document ID of Barack Obama’s article?
Answer:
Question 2: Which bin contains Barack Obama’s article? Enter its integer index.
Answer:
Question 3: Examine the bit representations of the bins containing Barack Obama and Joe Biden. In how many places do they agree?
- 16 out of 16 places (Barack Obama and Joe Biden fall into the same bin)
- 15 out of 16 places
- 13 out of 16 places
- 11 out of 16 places
- 9 out of 16 places
Question 4: Refer to the section “Effect of nearby bin search”. What was the smallest search radius that yielded the correct nearest neighbor for Obama, namely Joe Biden?
Answer:
Question 5: Suppose our goal was to produce 10 approximate nearest neighbors whose average distance from the query document is within 0.01 of the average for the true 10 nearest neighbors. For Barack Obama, the true 10 nearest neighbors are on average about 0.77. What was the smallest search radius for Barack Obama that produced an average distance of 0.78 or better?
Answer:
Week 3: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
Quiz 1: k-means
Question 1: (True/False) k-means always converges to a local optimum.
- True
- False
Question 2: (True/False) The clustering objective is non-increasing throughout a run of k-means.
- True
- False
Question 3: (True/False) Running k-means with a larger value of k always enables a lower possible final objective value than running k-means with smaller k.
- True
- False
Question 4: (True/False) Any initialization of the centroids in k-means is just as good as any other.
- True
- False
Question 5: (True/False) Initializing centroids using k-means++ guarantees convergence to a global optimum.
- True
- False
Question 6: (True/False) Initializing centroids using k-means++ costs more than random initialization in the beginning, but can pay off eventually by speeding up convergence.
- True
- False
Question 7: (True/False) Using k-means++ can only influence the number of iterations to convergence, not the quality of the final assignments (i.e., objective value at convergence).
- True
- False
Question 8: Consider the following dataset:
X1 | X2 | |
Data point 1 | -1.88 | 2.05 |
Data point 2 | -0.71 | 0.42 |
Data point 3 | 2.41 | -0.67 |
Data point 4 | 1.85 | -3.80 |
Data point 5 | -3.69 | -1.33 |
Perform k-means with k=2 until the cluster assignment does not change between successive iterations. Use the following initialization for the centroids:
X1 | X2 | |
Cluster 1 | 2.00 | 2.00 |
Cluster 2 | -2.00 | -2.00 |
Which of the five data points changed its cluster assignment most often during the k-means run?
- Data point 1
- Data point 2
- Data point 3
- Data point 4
- Data point 5
Question 9: Suppose we initialize k-means with the following centroids
Which of the following best describes the cluster assignment in the first iteration of k-means?
Answer:
Quiz 2: Clustering text data with K-means
Question 1: : (True/False) The clustering objective (heterogeneity) is non-increasing for this example.
- True
- False
Question 2: Let’s step back from this particular example. If the clustering objective (heterogeneity) would ever increase when running K-means, that would indicate: (choose one)
- K-means algorithm got stuck in a bad local minimum
- There is a bug in the K-means code
- All data points consist of exact duplicates
- Nothing is wrong. The objective should generally go down sooner or later.
Question 3: Refer to the output of K-means for K=3 and seed=0. Which of the three clusters contains the greatest number of data points in the end?
- Cluster #0
- Cluster #1
- Cluster #2
Question 4: Another way to capture the effect of changing initialization is to look at the distribution of cluster assignments. Compute the size (# of member data points) of clusters for each of the multiple runs of K-means.
Look at the size of the largest cluster (most # of member data points) across multiple runs, with seeds 0, 20000, …, 120000. What is the maximum value this quantity takes?
Answer:
Question 5: Refer to the section “Visualize clusters of documents”. Which of the 10 clusters above contains the greatest number of articles?
- Cluster 0: artists, books, him/his
- Cluster 4: music, orchestra, symphony
- Cluster 5: female figures from various fields
- Cluster 7: law, courts, justice
- Cluster 9: academia
Question 6: Refer to the section “Visualize clusters of documents”. Which of the 10 clusters above contains the least number of articles?
- Cluster 1: film, theater, tv, actor
- Cluster 3: elections, ministers
- Cluster 6: composers, songwriters, singers, music producers
- Cluster 7: law, courts, justice
- Cluster 8: football
Question 7: Another sign of too large K is having lots of small clusters. Look at the distribution of cluster sizes (by number of member data points). How many of the 100 clusters have fewer than 236 articles, i.e. 0.4% of the dataset?
Quiz 3: MapReduce for k-means
Question 1: Suppose we are operating on a 1D vector. Which of the following operation is not data parallel over the vector elements?
- Add a constant to every element.
- Multiply the vector by a constant.
- Increment the vector by another vector of the same dimension.
- Compute the average of the elements.
- Compute the sign of each element.
Question 2: (True/False) A single mapper call can emit multiple (key,value) pairs.
- True
- False
Question 3: (True/False) More than one reducer can emit (key,value) pairs with the same key simultaneously.
- True
- False
Question 4: (True/False) Suppose we are running k-means using MapReduce. Some mappers may be launched for a new k-means iteration even if some reducers from the previous iteration are still running.
- True
- False
Question 5: Consider the following list of binary operations. Which can be used for the reduce step of MapReduce? Choose all that apply.
Hints: The reduce step requires a binary operator that satisfied both of the following conditions.
- Commutative: OP(x_1,x_2) = OP(x_2,x_1)OP(x1,x2)=OP(x2,x1)
- Associative: OP(OP(x_1, x_2), x_3) = OP(x_1, OP(x_2, x_3))OP(OP(x1,x2),x3)=OP(x1,OP(x2,x3))
- OP1(x1,x2)=max(x1,x2)
- OP2(x1,x2)=x1+x2−2
- OP3(x1,x2)=3×1+2×2
- OP4(x1,x2)=x21+x2
- OP5(x1,x2)=(x1+x2)/2
Week 4: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
Quiz 1: EM for Gaussian mixtures
Question 1: (True/False) While the EM algorithm maintains uncertainty about the cluster assignment for each observation via soft assignments, the model assumes that every observation comes from only one cluster.
- True
- False
Question 2: (True/False) In high dimensions, the EM algorithm runs the risk of setting cluster variances to zero.
- True
- False
Question 3: In the EM algorithm, what do the E step and M step represent, respectively?
- Estimate cluster responsibilities, Maximize likelihood over parameters
- Estimate likelihood over parameters, Maximize cluster responsibilities
- Estimate number of parameters, Maximize likelihood over parameters
- Estimate likelihood over parameters, Maximize number of parameters
Question 4: Suppose we have data that come from a mixture of 6 Gaussians (i.e., that is the true data structure). Which model would we expect to have the highest log-likelihood after fitting via the EM algorithm?
- A mixture of Gaussians with 2 component clusters
- A mixture of Gaussians with 4 component clusters
- A mixture of Gaussians with 6 component clusters
- A mixture of Gaussians with 7 component clusters
- A mixture of Gaussians with 10 component clusters
Question 5: Which of the following correctlydescribes the differences between EM for mixtures of Gaussians and k-means? Choose all that apply.
- k-means often gets stuck in a local minimum, while EM tends not to
- EM is better at capturing clusters of different sizes and orientations
- EM is better at capturing clusters with overlaps
- EM is less prone to overfitting than k-means
- k-means is equivalent to running EM with infinitesimally small diagonal covariances.
Question 6: Suppose we are running the EM algorithm. After an E-step, we obtain the following responsibility matrix:
Cluster responsibilities | Cluster A | Cluster B | Cluster C |
Data point 1 | 0.20 | 0.40 | 0.40 |
Data point 2 | 0.50 | 0.10 | 0.40 |
Data point 3 | 0.70 | 0.20 | 0.10 |
Which is the most probable cluster for data point 3?
- Cluster A
- Cluster B
- Cluster C
Question 7: Suppose we are running the EM algorithm. After an E-step, we obtain the following responsibility matrix:
Cluster responsibilities | Cluster A | Cluster B | Cluster C |
Data point 1 | 0.20 | 0.40 | 0.40 |
Data point 2 | 0.50 | 0.10 | 0.40 |
Data point 3 | 0.70 | 0.20 | 0.10 |
Suppose also that the data points are as follows:
Dataset | X | Y | Z |
Data point 1 | 3 | 1 | 2 |
Data point 2 | 0 | 0 | 3 |
Data point 3 | 1 | 3 | 7 |
Let us compute the new mean for Cluster A. What is the Z coordinate of the new mean? Round your answer to 3 decimal places.
Question 8: Which of the following contour plots describes a Gaussian distribution with diagonal covariance? Choose all that apply.
- (1)
- (2)
- (3)
- (4)
- (5)
Question 9: Suppose we initialize EM for mixtures of Gaussians (using full covariance matrices) with the following clusters:
Which of the following best describes the updated clusters after the first iteration of EM?
Quiz 2: Implementing EM for Gaussian mixtures
Question 1: What is the weight that EM assigns to the first component after running the above codeblock? Round your answer to 3 decimal places.
Question 2: Using the same set of results, obtain the mean that EM assigns the second component. What is the mean in the first dimension? Round your answer to 3 decimal places.
Question 3: Using the same set of results, obtain the covariance that EM assigns the third component. What is the variance in the first dimension? Round your answer to 3 decimal places.
Question 4: Is the loglikelihood plot monotonically increasing, monotonically decreasing, or neither?
- Monotonically increasing
- Monotonically decreasing
- Neither
Question 5: Calculate the likelihood (score) of the first image in our data set (img[0]) under each Gaussian component through a call to `multivariate_normal.pdf`. Given these values, what cluster assignment should we make for this image?
- Cluster 0
- Cluster 1
- Cluster 2
- Cluster 3
Question 6: Four of the following images are not in the list of top 5 images in the first cluster. Choose these four.
Answer:
Quiz 3: Clustering text data with Gaussian mixtures
Question 1: Select all the topics that have a cluster in the model created above.
- Baseball
- Basketball
- Soccer/football
- Music
- Politics
- Law
- Finance
Question 2: Try fitting EM with the random initial parameters you created above. What is the final loglikelihood that the algorithm converges to? Choose the range that contains this value.
- Less than 2.2e9
- Between 2.2e9 and 2.3e9
- Between 2.3e9 and 2.4e9
- Between 2.4e9 and 2.5e9
- Greater than 2.5e9
Question 3: Is the final loglikelihood larger or smaller than the final loglikelihood we obtained above when initializing EM with the results from running k-means?
- Initializing EM with k-means led to a larger final loglikelihood
- Initializing EM with k-means led to a smaller final loglikelihood
Question 4: For the above model, `out_random_init`, use the `visualize_EM_clusters` method you created above. Are the clusters more or less interpretable than the ones found after initializing using k-means?
- More interpretable
- Less interpretable
Week 5: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
Quiz 1: Latent Dirichlet Allocation
Question 1: (True/False) According to the assumptions of LDA, each document in the corpus contains words about a single topic.
- True
- False
Question 2: (True/False) Using LDA to analyze a set of documents is an example of a supervised learning task.
- True
- False
Question 3: (True/False) When training an LDA model, changing the ordering of words in a document does not affect the overall joint probability.
- True
- False
Question 4: (True/False) Suppose in a trained LDA model two documents have no topics in common (i.e., one document has 0 weight on any topic with non-zero weight in the other document). As a result, a single word in the vocabulary cannot have high probability of occurring in both documents.
- True
- False
Question 5: (True/False) Topic models are guaranteed to produce weights on words that are coherent and easily interpretable by humans.
- True
- False
Quiz 2: Learning LDA model via Gibbs sampling
Question 1: (True/False) Each iteration of Gibbs sampling for Bayesian inference in topic models is guaranteed to yield a higher joint model probability than the previous sample.
- True
- False
Question 2: (Check all that are true) Bayesian methods such as Gibbs sampling can be advantageous because they
- Account for uncertainty over parameters when making predictions
- Are faster than methods such as EM
- Maximize the log probability of the data under the model
- Regularize parameter estimates to avoid extreme values
Question 3: For the standard LDA model discussed in the lectures, how many parameters are required to represent the distributions defining the topics?
- [# unique words]
- [# unique words] * [# topics]
- [# documents] * [# unique words]
- [# documents] * [# topics]
Question 4: Suppose we have a collection of documents, and we are focusing our analysis to the use of the following 10 words. We ran several iterations of collapsed Gibbs sampling for an LDA model with K=2 topics and alpha=10.0 and gamma=0.1 (with notation as in the collapsed Gibbs sampling lecture). The corpus-wide assignments at our most recent collapsed Gibbs iteration are summarized in the following table of counts:
Word | Count in topic 1 | Count in topic 2 |
baseball | 52 | 0 |
homerun | 15 | 0 |
ticket | 9 | 2 |
price | 9 | 25 |
manager | 20 | 37 |
owner | 17 | 32 |
company | 1 | 23 |
stock | 0 | 75 |
bankrupt | 0 | 19 |
taxes | 0 | 29 |
We also have a single document ii with the following topic assignments for each word:
topic | 1 | 2 | 1 | 2 | 1 |
word | baseball | manager | ticket | price | owner |
Suppose we want to re-compute the topic assignment for the word “manager”. To sample a new topic, we need to compute several terms to determine how much the document likes each topic, and how much each topic likes the word “manager”. The following questions will all relate to this situation.
First, using the notation in the slides, what is the value of m_{\text{manager}, 1}mmanager,1 (i.e., the number of times the word “manager” has been assigned to topic 1)?
Answer:
Question 5: Consider the situation described in Question 4.
What is the value of \sum_w m_{w, 1}∑wmw,1, where the sum is taken over all words in the vocabulary?
Answer:
Question 6: Consider the situation described in Question 4.
Following the notation in the slides, what is the value of n_{i, 1}ni,1 for this document ii (i.e., the number of words in document ii assigned to topic 1)?
Answer:
Question 7: In the situation described in Question 4, “manager” was assigned to topic 2. When we remove that assignment prior to sampling, we need to decrement the associated counts.
After decrementing, what is the value of n_{i, 2}ni,2?
Answer:
Question 8: In the situation described in Question 4, “manager” was assigned to topic 2. When we remove that assignment prior to sampling, we need to decrement the associated counts.
After decrementing, what is the value of m_{manager, 2}mmanager,2?
Answer:
Quiz 3: Modeling text topics with Latent Dirichlet Allocation
Question 1: Identify the top 3 most probable words for the first topic.
- institute
- university
- president
- business
- board
- game
- coach
Question 2: What is the sum of the probabilities assigned to the top 50 words in the 3rd topic? Round your answer to 3 decimal places.
Answer:
Question 3: What is the topic most closely associated with the article about former US President George W. Bush? Use the average results from 100 topic predictions.
Answer:
Question 4: What are the top 3 topics corresponding to the article about English football (soccer) player Steven Gerrard? Use the average results from 100 topic predictions.
- international athletics
- team sports
- general music
- Great Britain and Australia
- science and research
Question 5: What was the value of alpha used to fit our original topic model?
Answer:
Question 6: What was the value of gamma used to fit our original topic model? Remember that Turi Create uses “beta” instead of “gamma” to refer to the hyperparameter that influences topic distributions over words.
Answer
Question 7: How many topics are assigned a weight greater than 0.3 or less than 0.05 for the article on Paul Krugman in the low alpha model? Use the average results from 100 topic predictions.
Answer
Question 8: How many topics are assigned a weight greater than 0.3 or less than 0.05 for the article on Paul Krugman in the high alpha model? Use the average results from 100 topic predictions.
Answer:
Question 9: For each topic of the low gamma model, compute the number of words required to make a list with total probability 0.5. What is the average number of words required across all topics? (HINT: use the get_topics() function from Turi Create with the cdf_cutoff argument.)
Answer:
Question 10: For each topic of the high gamma model, compute the number of words required to make a list with total probability 0.5. What is the average number of words required across all topics? (HINT: use the get_topics() function from Turi Create with the cdf_cutoff argument).
Answer:
Week 6: Machine Learning: Clustering & Retrieval Coursera Quiz Answers
Quiz 1: Modeling text data with a hierarchy of clusters
Question 1: Which diagram best describes the hierarchy right after splitting the athletes cluster?
Answer:
Question 2: Let us bipartition the clusters female figures and politicians & government officials. Which diagram best describes the resulting hierarchy of clusters for the non-athletes?
Note. The cluster for the athletes and artists are not shown to save space.
Answer:
More About This Course
Finding Similar Documents in Case Studies
A reader is interested in a specific news story, and you wish to recommend similar stories. What is the correct definition of similarity? What if there are millions of additional documents? Do you have to browse through all other documents every time you want to obtain a new document? How do you group documents that are similar? How can you learn about new and evolving subjects included in the documents?
In this third case study, locating related documents, you will investigate similarity-based retrieval techniques. This course will also look at structured representations for representing documents in a corpus, such as clustering and mixed membership models like latent Dirichlet allocation (LDA). You will use expectation maximization (EM) to discover how to cluster documents and scale the approaches using MapReduce.
Learning Objectives: After this course, you will be able to:
- -Build a document retrieval system based on k-nearest neighbors.
- -Determine multiple text data similarity metrics.
- -Use KD-trees to reduce computations in the k-nearest neighbor search.
- -Use locality-sensitive hashing to compute approximate nearest neighbors.
- -Consider the differences between supervised and unsupervised learning activities.
- -Use k-means to group papers by topic.
- -Explain how to use MapReduce to parallelize k-means.
- -Investigate probabilistic clustering algorithms based on mixture models.
- -Use expectation maximization to fit a Gaussian mixture model (EM).
- -Use latent Dirichlet allocation to do mixed membership modeling (LDA).
- -Explain the steps of a Gibbs sampler and how to use the results to derive conclusions.
- -Compare and contrast non-convex optimization initialization strategies.
- -Python should be used to implement these strategies.
SKILLS YOU WILL GAIN
- Data Clustering Algorithms
- K-Means Clustering
- Machine Learning
- K-D Tree
Conclusion
Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Machine Learning: Clustering & Retrieval Quiz of Coursera and grab some premium knowledge with less effort. If this article helped you in any way then make sure to share it with your friends on social media and let them know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.
I’m no longer certain the place you’re getting your info, however good topic. I must spend a while studying more or understanding more. Thank you for magnificent information I was looking for this information for my mission.
The popularity of machine learning has grown exponentially in recent years. This is due to its ability to automate and optimize complex processes, as well as its ability to learn from data and make predictions.
Simply desire to say your article is as surprising. The clearness on your submit is simply spectacular and that i can suppose you are a professional in this subject. Fine together with your permission allow me to grasp your RSS feed to keep updated with approaching post. Thanks one million and please carry on the gratifying work.
I really like your blog.. very nice colors & theme. Did you design this website yourself or did you hire someone to do it for you? Plz respond as I’m looking to create my own blog and would like to find out where u got this from. kudos
What i do not realize is actually how you’re not really much more well-liked than you may be right now. You’re so intelligent. You realize therefore considerably relating to this subject, made me personally consider it from numerous varied angles. Its like men and women aren’t fascinated unless it is one thing to do with Lady gaga! Your own stuffs excellent. Always maintain it up!
Thanx for the effort, keep up the good work Great work, I am going to start a small Blog Engine course work using your site I hope you enjoy blogging with the popular BlogEngine.net.Thethoughts you express are really awesome. Hope you will right some more posts.
hi!,I like your writing so much! share we keep up a correspondence extra about your post on AOL? I need an expert in this area to resolve my problem. Maybe that is you! Taking a look ahead to see you.
But wanna tell that this is invaluable, Thanks for taking your time to write this.
Hey there! Would you mind if I share your blog with my zynga group? There’s a lot of folks that I think would really enjoy your content. Please let me know. Thanks
I think other web site proprietors should take this site as an model, very clean and great user friendly style and design, let alone the content. You are an expert in this topic!
order cialis 20mg pills buy tadalafil 20mg without prescription buy generic ed pills
I’m not sure why but this web site is loading incredibly slow for me. Is anyone else having this problem or is it a problem on my end? I’ll check back later on and see if the problem still exists.
duricef online order epivir buy online buy finasteride for sale
estrace 1mg drug estrace 1mg usa buy generic prazosin online
fluconazole for sale cheap ciprofloxacin 1000mg cipro pill
where can i buy mebendazole retin order online purchase tadalis for sale
order metronidazole 200mg for sale buy metronidazole paypal order cephalexin pill
avanafil 200mg over the counter diclofenac 100mg cheap purchase diclofenac pill
cleocin pills erythromycin price purchase fildena pills
indocin capsule brand indocin 50mg buy cefixime 200mg generic
purchase trimox for sale buy arimidex 1mg generic order clarithromycin 500mg without prescription
oral catapres 0.1mg buy clonidine 0.1mg pill purchase spiriva pill
suhagra 100mg generic sildenafil 50mg usa sildenafil 100mg tablets
minocycline 100mg ca actos 30mg pills oral actos 30mg
buy generic accutane 40mg generic zithromax 500mg order zithromax pill
arava 20mg without prescription azulfidine pills order sulfasalazine 500mg generic
us cialis sales brand viagra overnight delivery for cialis
azipro 500mg pills neurontin 800mg usa order neurontin 100mg without prescription
ivermectin uk free ed pills prednisone 10mg cheap
order furosemide 100mg online buy doxycycline medication best allergy for itchy skin
levitra pills tizanidine 2mg pills hydroxychloroquine uk
buy altace pills for sale arcoxia 60mg generic buy etoricoxib 60mg generic
levitra oral buy tizanidine 2mg generic purchase plaquenil generic
generic asacol 400mg azelastine drug avapro 300mg brand
buy olmesartan 10mg sale buy generic benicar divalproex 500mg uk
clobetasol online order amiodarone 200mg generic cordarone 200mg oral
purchase acetazolamide generic azathioprine sale buy azathioprine 50mg online
coreg without prescription chloroquine pills order chloroquine 250mg sale
lanoxin oral purchase molnunat pills buy molnupiravir 200 mg generic
buy naprosyn 250mg sale naprosyn 250mg cheap buy lansoprazole
albuterol cheap protonix 40mg cheap buy pyridium generic
baricitinib 2mg usa buy olumiant 2mg pills order generic atorvastatin 10mg
cost montelukast 10mg symmetrel without prescription buy avlosulfon paypal
buy adalat 30mg pill buy nifedipine for sale allegra 120mg pills
amlodipine 5mg pill lisinopril without prescription prilosec online
You have mentioned very interesting details! ps decent website . “‘We’re always lucky,’ I said and like a fool I did not knock on wood.” by Ernest Hemingway.
dapoxetine 90mg brand order cytotec online buy xenical 120mg pill
buy lopressor online generic metoprolol 50mg buy medrol sale
buy diltiazem generic buy generic acyclovir order allopurinol 100mg online
buy triamcinolone generic buy claritin online buy generic claritin
crestor 10mg over the counter order zetia online cheap motilium price
purchase sumycin sale buy sumycin pill buy ozobax
ampicillin 500mg cost buy ampicillin antibiotic flagyl without prescription
buy toradol generic buy inderal 20mg pills buy inderal medication
brand bactrim 960mg purchase bactrim for sale purchase cleocin online
I am really enjoying the theme/design of your site. Do you ever run into any internet browser compatibility problems? A couple of my blog visitors have complained about my website not working correctly in Explorer but looks great in Safari. Do you have any suggestions to help fix this issue?
buy plavix 150mg sale buy methotrexate online cheap order coumadin pill
erythromycin pills order fildena 50mg brand nolvadex 10mg
purchase reglan pills generic hyzaar buy generic esomeprazole online
rhinocort medication order cefuroxime generic buy careprost online
topiramate generic purchase sumatriptan without prescription buy levaquin without a prescription
buy methocarbamol medication suhagra pill purchase sildenafil
avodart over the counter buy mobic 7.5mg online cheap order meloxicam 7.5mg online cheap
purchase aldactone for sale buy aldactone paypal valacyclovir 500mg us
buy finasteride 5mg sale viagra 100 mg viagra pills
tretinoin gel ca avanafil 200mg over the counter buy avanafil without prescription
order tadalafil pill buy cialis 40mg generic cheap viagra online
tadalafil medication buy cambia pills purchase indomethacin generic
brand cialis pills cialis usa buy ed pills online usa
lamisil 250mg ca lamisil 250mg cost purchase trimox sale
purchase azulfidine generic benicar 10mg pill verapamil 240mg us
order arimidex 1 mg online cheap order clonidine 0.1 mg pill buy generic catapres for sale
buy divalproex without a prescription isosorbide pill imdur 40mg sale
meclizine 25mg brand oral minomycin minocycline 100mg us
purchase movfor generic purchase molnunat sale buy omnicef 300mg generic
medication for ed sildenafil 50mg uk viagra 100mg pills for sale
order lansoprazole 30mg pill cost albuterol 100mcg purchase pantoprazole sale
causes of erectile dysfunction tadalafil 10mg brand real cialis pharmacy prescription
phenazopyridine cheap amantadine 100mg brand buy symmetrel 100 mg online cheap
generic dapsone 100mg order dapsone 100 mg for sale buy aceon 8mg online
order allegra 180mg pill altace 10mg without prescription buy glimepiride 1mg sale
order etoricoxib 120mg online cheap arcoxia azelastine 10 ml us
hytrin price buy pioglitazone for sale cialis for sales
buy irbesartan paypal buy avapro 150mg without prescription brand buspirone
amiodarone 100mg brand carvedilol 6.25mg drug dilantin 100mg pills
order albenza pill buy albenza 400 mg online cheap provera online
purchase oxytrol endep sale fosamax 35mg over the counter
buy praziquantel online cheap microzide 25mg periactin 4 mg usa
cheap luvox 100mg duloxetine 20mg usa buy duloxetine 20mg pill
order nitrofurantoin generic buy motrin 600mg pill purchase pamelor generic
purchase glipizide generic order betamethasone betnovate 20 gm price
buy anacin paypal paxil 10mg ca pepcid 20mg tablet
buy generic clomipramine oral clomipramine progesterone 100mg cheap
purchase tacrolimus sale cheap tacrolimus order ropinirole 2mg online
buy calcitriol 0.25 mg without prescription order generic trandate 100 mg fenofibrate 160mg sale
buy dexamethasone 0,5 mg generic nateglinide price buy starlix pill
order oxcarbazepine 300mg sale order trileptal 300mg generic urso 150mg canada
captopril 25 mg pill purchase candesartan online purchase tegretol online
buy bupropion 150mg for sale zyban us strattera 25mg price
ciprofloxacin 500mg cost ciprofloxacin medication cefadroxil buy online
buy seroquel 100mg sale sertraline 50mg sale lexapro online buy
order lamivudine online cheap order epivir online cheap quinapril 10 mg cost
order frumil 5mg for sale zovirax sale acivir cheap
prozac oral order fluoxetine 40mg for sale purchase letrozole sale
order zebeta online bisoprolol tablet buy oxytetracycline generic
valaciclovir oral valaciclovir 1000mg sale cost floxin 200mg
order levetiracetam 500mg online bactrim 480mg sale sildenafil 50mg us
order vantin 200mg pills purchase theo-24 Cr generic order flixotide without prescription
zaditor price buy ketotifen without prescription buy tofranil sale
order mintop without prescription cialis price best ed pill
I just like the valuable info you provide on your articles. I will bookmark your weblog and test once more here regularly. I am quite certain I will be told a lot of new stuff proper here! Good luck for the following!
order acarbose 25mg pills buy glyburide 5mg sale order fulvicin 250 mg sale
meloset 3 mg cheap buy meloset 3mg sale buy danocrine pills
order dipyridamole for sale order pravachol 10mg generic buy generic pravachol over the counter
order dydrogesterone online buy sitagliptin 100mg online purchase jardiance for sale
I’m really inspired with your writing abilities as well as with
the layout to your weblog. Is that this a paid theme or did you customize it your self?
Anyway stay up the excellent quality writing, it is uncommon to look a great weblog
like this one these days..
how to get monograph without a prescription buy generic cilostazol buy pletal cheap
buy fludrocortisone pills order loperamide online loperamide for sale online
buy generic prasugrel buy generic dramamine 50mg buy tolterodine 2mg generic
mestinon cheap rizatriptan 5mg for sale rizatriptan 10mg pills
buy ferrous sulfate 100mg pills order generic ferrous 100mg sotalol 40mg us
My developer is trying to persuade me to move to .net from PHP.
I have always disliked the idea because of the costs.
But he’s tryiong none the less. I’ve been using WordPress on various websites for about a year and am concerned about switching to another platform.
I have heard very good things about blogengine.net. Is there a way I
can import all my wordpress content into it?
Any help would be greatly appreciated!
cost xalatan how to get rivastigmine without a prescription order rivastigmine 6mg without prescription
vasotec usa order doxazosin 2mg without prescription buy lactulose medication
Your method of explaining the whole thing in this post is actually
pleasant, all be able to effortlessly know it, Thanks a lot.
buy premarin 600 mg without prescription purchase premarin online viagra 50mg usa
order prilosec 10mg online cheap buy lopressor 50mg pills buy lopressor 50mg pill
buy generic micardis for sale plaquenil medication order molnupiravir 200 mg generic
cenforce 50mg sale order naproxen 500mg generic chloroquine pills
where can i buy modafinil modafinil order order deltasone 10mg sale
buy isotretinoin online buy isotretinoin sale order azithromycin 500mg online cheap
purchase omnicef sale buy omnicef no prescription order lansoprazole generic
azithromycin 500mg cost brand prednisolone buy neurontin without prescription
atorvastatin 80mg for sale buy generic atorvastatin amlodipine oral
roulette free furosemide tablet furosemide 40mg price
blackjack online ventolin 4mg canada buy ventolin cheap
protonix 20mg cheap zestril ca buy generic phenazopyridine 200 mg
play roulette free for fun online blackjack real money ivermectin 3 mg over counter
real money online blackjack brand levothroid synthroid 150mcg brand
amantadine 100 mg us buy amantadine without a prescription avlosulfon price
buy clomid 50mg pill imuran cost order imuran 50mg generic
brand levitra 10mg buy lanoxin 250mg online cheap tizanidine online
buy methylprednisolone medication nifedipine 10mg cost aristocort uk
For years, companies have structured their business strategies around the funnel, but not anymore
phenytoin us brand ditropan 5mg oral oxytrol
perindopril where to buy coversyl price buy allegra cheap
baclofen 25mg pills endep 50mg for sale where can i buy toradol
buy cheap generic ozobax brand baclofen buy ketorolac cheap
buy claritin claritin pill purchase dapoxetine pill
buy fosamax 70mg nitrofurantoin 100mg pills oral furadantin 100mg
buy inderal 10mg online oral plavix buy plavix 150mg online
order pamelor generic pamelor 25 mg over the counter purchase acetaminophen
cheap amaryl purchase etoricoxib online purchase arcoxia pill
generic warfarin 5mg buy coumadin sale metoclopramide without prescription
xenical 60mg cheap buy generic mesalamine brand diltiazem 180mg
famotidine pills losartan generic how to buy tacrolimus
buy esomeprazole tablets order topiramate 200mg online cheap buy topamax paypal
purchase azelastine generic order zovirax 400mg avalide online order
imitrex 25mg pill avodart uk avodart ca
buy cheap allopurinol order zyloprim 300mg without prescription order crestor 10mg pills
buy zantac online meloxicam for sale buy celebrex cheap
buy buspar tablets buy cordarone 100mg sale cheap amiodarone 200mg
flomax uk simvastatin order online simvastatin 10mg cost
aldactone 25mg cheap buy valtrex 1000mg sale order proscar generic
letter editing order an essay online buy term papers online
buy diflucan 200mg where to buy diflucan without a prescription cipro online
cost aurogra 50mg buy estradiol online cheap estrace canada
metronidazole 400mg us order metronidazole cephalexin ca
buy lamotrigine 200mg generic prazosin medication order mebendazole 100mg
nolvadex 20mg usa order betahistine 16 mg generic buy symbicort inhalers for sale
order tretinoin gel retin cream usa avana 200mg usa
buy axetil paypal how to buy cefuroxime how to get methocarbamol without a prescription
tadalafil over the counter buy indomethacin 75mg online order indocin 75mg sale
buy desyrel 100mg generic desyrel over the counter clindamycin canada
aspirin 75 mg usa american online casino real money casino slots gambling
purchase terbinafine sale bet and win money online online casino usa
my future essay writing best casino games online blackjack free
buy dissertation online masters degree dissertation suprax 100mg oral
calcitriol medication buy trandate generic purchase tricor generic
Attractive section of content. I just stumbled upon your web site and in accession capital to assert that
I acquire in fact enjoyed account your blog posts.
Anyway I’ll be subscribing to your feeds and even I achievement you access consistently rapidly.
permanent acne removal treatment prescription strength acne medication oxcarbazepine 300mg pill
buy generic catapres online tiotropium bromide 9 mcg generic order tiotropium bromide online
buy uroxatral cheap best medicine in stomach pain acid medication list
buy minocycline tablets requip 2mg pills ropinirole 1mg drug
buying sleeping tablets on internet buy sleeping pills online canada gnc weight loss supplements reviews
cheap letrozole 2.5 mg buy abilify 30mg generic aripiprazole pills
taking medication like chewing tobacco osteoporosis medication once a week strong pain killers with script
buy antiviral drugs example of antivirals drugs sugar blocker pills at walmart
periactin pills ketoconazole 200mg pills order ketoconazole 200mg sale
skin fungal infection treatment oral is 100 mg losartan a high dosage best bp meds for elderly
Thank you very much for sharing, I learned a lot from your article. Very cool. Thanks. nimabi