Week 2 – Machine Learning Research

Feb 28, 2019

This week, I learned even more about the Weka machine learning system. I am working on a tutorial that teaches about the various features of Weka.

Two important types of machine learning algorithms are nearest-neighbor learning and decision trees. Nearest-neighbor learning involves classifying data points using the instances closest to them, and decision trees are something like using if-then statements based on the values of attributes to classify each data point. Both of these are affected by various factors. In addition, besides using these tools, I viewed visualizations of how they worked.

I am still waiting to receive another student performance data set. Soon, I will have learned enough to start working on the data set I will use for this project.

2 Replies to “Week 2 – Machine Learning Research”

  1. Sahil J. says:

    Both of those machine learning algorithms are great! Visualization is important in an ML field that has become somewhat of a blackbox.

    In fact, while you’re waiting on a dataset, I’d suggest getting some practice on some that you can find online (UCI has a Machine Learning repository, Kaggle is another really good resource). Here’s one that I found: https://archive.ics.uci.edu/ml/datasets/student+performance.

    Hope it helps and good luck!

  2. Savinay C. says:

    Those two machine learning algorithms are definitely important. Looks like your getting great progress done!

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