# SIT720 - Machine Learning - Digital literacy - Critical Thinking - Problem Solving - Computer Science - Assessment Answer

Order Code: MAS20592
• Subject Code :

SIT720 Machine Learning

• University :
• Country :

Australia

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###### SIT720 - Machine Learning - Digital literacy - Critical Thinking - Problem Solving -  Computer Science - Assessment Answer

Perform unsupervised learning of data such as clustering and dimensionality reduction

Learning Outcome

GLO 1: Discipline knowledge and capabilities
GLO 3: Digital literacy
GLO 4: Critical thinking
GLO 5: Problem-solving

Purpose

In this assignment, this is an individual assessment task of maximum of 20 pages including all relevant material, graphs, images, and tables. Students will be required to provide responses for a series of problem situations related to their analysis techniques. They are also required to provide evidence through the articulation of the scenario, application of Python programming skills, analysis techniques and provide a rationale for their response.eed to demonstrate your skills for data clustering and dimensionality reduction. There are two parts to this assignment.

Part-1 Clustering:

Instructions: there are five different files where each file contains a different number and types of digit images. The file name ends with a digit between 0 to 4. Please compute the modulus operation (fID=SID % 5), where SID is your own student ID number. Now select the data file, name of which ends with the same fID value. For example, if your student id is 218201419, then you should compute fID=218201419%5. This result is fID=4 so in this case you should work with the file named "digitData4.csv'. If the result was fID=2 you must work with the file named “digitData2.csv”.

1- Read the downloaded file into a matrix M(mXn). Create an empty numpy array X with m rows and n-1 columns. Assign all m rows and first n-1 columns of M into X. Create a numpy vector true labels and assign n-th column of M into that. Print dimensions of M, X and true labels.
2- Next perform K-means clustering with 5 clusters using Euclidean distance as a similarity measure. Evaluate the clustering performance using adjusted rand index (ARI) and adjusted mutual information. Report the clustering performance averaged over 50 random initializations of K-means.
3- If we have an ARI value of 0.7 after a single run of K-means clustering with 'Kmeans++' initializaton for any data set then what will be the value of averaged ARI over 20 repetitions. Explain why?
4- Repeat K-means clustering with 5 clusters using a similarity measure other than Euclidean distance (you are free to use other libraries). Evaluate the clustering performance over 50 random initializations of K-means using adjusted rand index and adjusted mutual information. Report the clustering performance and compare it with the results obtained in step 2.

Part-2 Dimensionality Reduction using PCA/SVD: For the provided digits dataset:
1- Perform PCA. Plot the captured variance with respect to increasing latent dimensionality. What is the minimum dimension that captures at least 95% variance?
2- Create a scatter plot with each of the total rows of X projected onto the first two principal components. In other words, the horizontal axis should be v1, the vertical axisv2, and each individual should be projected onto the subspace spanned by v1 and v2. Your plot must use a different color for each digit and include a legend.

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• Posted on : December 16th, 2018

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