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1 Extra Credit Suggestions Listed below are some categories and suggestions for

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1 Extra Credit Suggestions
Listed below are some categories and suggestions for extra credit. You may utilize one of these,
or propose your own.
1.1 Research and Report
Research a topic or example, and write a report. Some suggestions:
• You are given a 100 books, some of which are science books and some of which are science fiction books. You want to classify the books into two classes. Describe the methodology of how you would do so. Note that PDF data is unstructured data. Therefore, what
are the various machine learning steps or tasks needed to perform the classification.
• Describe in details how the DBSCAN clustering algorithm works.
• Describe how you would extract features from images. Here is a Wiki link:
https : //en.wikipedia.org/wiki/F eature extraction
• Describe how convolutional neural networks (CNN) work. You may utilize the link https :
//adeshpande3.github.io/A−Beginner%27s−Guide−T o−Understanding−Convolutional−
Neural − Networks/
1.2 Investigation and Analysis
Investigate and analyze further an example posted in the lectures, by utilizing the Clustering
Investigations posted on the Blackboard. Some suggestions:
• In the lecture on Understanding Clustering, the wholesale customer data is clustered
using the DBSCAN algorithm. Interpret the results of the clustering. You may want to
zoom in on the plots.
• In the lecture on Understanding Clustering, multivariate normal distribution (MVN)
data is clustered using the hierarchical clustering algorithm. Investigate this example further, such as changing the mean and the covariance matrix parameters, and observe and
analyze the new results.
1.3 Implement or Develop Algorithms
In the investigation part, try a different data set, or develop your own code. Some suggestions:
• Choose another data set from the UCI Machine Learning repository, and perform clustering
task, using one of the clustering algorithms. Utilize the Clustering Investigations posted
on the Blackboard. Choose a data set with not too many features and samples. Some suggested data sets are UCI Sales Transactions and UCI Vertebral Column.
• Write your own code for the k-means algorithm and test it on a small data set, such as Iris
Flower.
1.4 Machine Learning Resources
• UCI Machine Learning Repository for Data Sets:
https : //archive.ics.uci.edu/ml/datasets.php
• Analytics Vidhya – Machine Learning Projects
https : //www.analyticsvidhya.com/blog/2018/05/24−ultimate−data−science−projects−
to − boost − your − knowledge − and − skills/
• Elite Data Science – Machine Learning Projects
https : //elitedatascience.com/machine − learning − projects − for − beginners#neural −
network
• Nielsen Deep Learning Tutorial
http : //neuralnetworksanddeeplearning.com/chap1.html
• Deshpande Convolutional Neural Network Tutorial
https : //adeshpande3.github.io/A − Beginner%27s − Guide − T o − Understanding −
Convolutional − Neural − Networks/
1.4 Machine Learning Resources
• UCI Machine Learning Repository for Data Sets:
https : //archive.ics.uci.edu/ml/datasets.php
• Analytics Vidhya – Machine Learning Projects
https : //www.analyticsvidhya.com/blog/2018/05/24−ultimate−data−science−projects−
to − boost − your − knowledge − and − skills/
• Elite Data Science – Machine Learning Projects
https : //elitedatascience.com/machine − learning − projects − for − beginners#neural −
network
• Nielsen Deep Learning Tutorial
http : //neuralnetworksanddeeplearning.com/chap1.html
• Deshpande Convolutional Neural Network Tutorial
https : //adeshpande3.github.io/A − Beginner%27s − Guide − T o − Understanding −
Convolutional − Neural − Networks/
Feature Extraction: https://en.wikipedia.org/wiki/Feature_extraction
Mutual Informtion: https://en.wikipedia.org/wiki/Mutual_information
DBSCAN Clustering: https://towardsdatascience.com/dbscan-clustering-explained-97556a2ad556
Nielsen – Deep Learning: http://neuralnetworksanddeeplearning.com/
Generative Adversarial Networks: https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29

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