Machine Learning (Stanford) Coursera Unsupervised Learning Quiz (Week 8, Quiz 1) for the github repo: https://github.com/mGalarnyk/datasciencecoursera/tree/master/Stanford_Machine_Learning

UnsupervisedLearningStanfordCoursera.md

Machine Learning Week 8 Quiz 1 (Unsupervised Learning) Stanford Coursera

Github repo for the Course: Stanford Machine Learning (Coursera)
Quiz Needs to be viewed here at the repo (because the image solutions cant be viewed as part of a gist)

Question 1

True or False	Statement	Explanation
False	Given historical weather records, predict if tomorrow's weather will be sunny or rainy	K-means cannot make classification predictions as it does not label its inputs.
True	Given a set of news articles from many different websites, find out what topics are the main topics covered	You can use K-means to cluster, and each cluster will correspond to a different market segment.
True	From the user usage patterns on a website, figure out what different groups of users exists.	You can use K-means to cluster users with each cluster corresponding to a different market segment.
False	Given many emails, you want to determine if they are Spam or Non-Spam emails.	K-means cannot make classification predictions as it does not label its inputs
True	Given a database of information about your users, automatically group them into different market segments.	You can use K-means to cluster the database entries, and each cluster will correspond to a different market segment.
True	Given sales data from a large number of products in a supermarket, figure out which products tend to form coherent groups (say are frequently purchased together) and thus should be put on the same shelf.	Market Segmentation.
False	Given sales data from a large number of products in a supermarket, estimate future sales for each of these products.	Such a prediction is a regression problem, and K-means does not use labels on the data, so it cannot perform regression.

Question 2

Answer	Explanation
c(i) = 1	x(i) is closest to μ1, so c(i) = 1

Question 3

True or False	Statement	Explanation
False	Randomly initialize the cluster centroids	Done earlier
False	Test on the cross-validation set	Any sort of testing is outside the scope of K-means algorithm itself
True	Move the cluster centroids, where the centroids, μk are updated	The cluster update is the second step of the K-means loop
True	The cluster assignment step, where the parameters c(i) are updated	This is the correst first step of the Kmeans loop

Question 4

Answer	Explanation
	This is the distortion cost function which we seek to minimize

Question 5

True or False	Statement	Explanation
False	Once an example has been assigned to a particular centroid, it will never be reassigned to another centroid	Not sure yet
True	A good way to initialize K-means is to select K (distinct) examples from the training set and set the cluster centroids equal to these selected examples.	This is the recommended method of initialization.
True	On every iteration of K-means, the cost funtion J(c(1), ..., c(m), μ1, ..., μk (the distortion function) should either stay the same or decrease; in particular, it should not increase	True
False	K-Means will always give the same results regardless of the initialization of the centroids.	K-means is sensitive to different initializations, which is why you should run it multiple times from different random initializations
True	For some datasets, the "right" or "correct" value of K (the number of clusters) can be ambiguous, and hard even for a human expert looking carefully at the data to decide.	Look at an elbow curve for an example. It can often be ambiguous.
True	If we are worried about K-means getting stuck in bad local optima, one way to ameliorate (reduce) this problem is if we try using multiple random initializations.	None needed