Cover Latent Variables Models and Neural Networks - IT/Computer Science Assignment Help

Assignment Task:

Task:

Objectives This assignment consists of three parts (A,B,C), which cover latent variables models and neural networks (Modules 4 and 5). The total marks of this assessment are 100 and will contribute 16% to your final score.

Part A. Document Clustering In this part, you solve a document clustering problem using unsupervised learning algorithms (i.e., soft and hard Expectation Maximization) for document clustering.

Question 1 [EM for Document Clustering, 40 Marks] I. Derive Expectation and Maximization steps of the hard-EM algorithm for Document Clustering, show your work in your submitted PDF report. In particular, include all model parameters that should be learnt and the exact expression (using the same math convention that we saw in the Module 4) that should be used to update these parameters during the learning process (ie., E step, M step and assignments). II. Implement the hard-EM (you derived above) and soft-EM (derived in Chapter 5 of Module

4). Please provide enough comments in your submitted code.

2 Hint: If it helps, feel free to base your code on the provided code for EM algorithm for GMM in Activity 4.1 or the codebase provided in the Moodle).

III. Load Task2A.txt file and necessary libraries (if needed, perform text preprocessing similar to what we did in Activity 4.2), set the number of clusters K=4, and run both the soft-EM and hard-EM algorithms on the provided data.

IV. Perform a PCA on the clusterings that you get based on the hard-EM and soft-EM in the same way we did in Activity

4.2. Then, visualize the obtained clusters with different colors where x and y axes are the first two principal components (similar to Activity 4.2). Attach the plots to your PDF report and report how and why the hard and soft-EM are different, based on your plots in the report.

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