CS 4364 Lab 2

Due Wednesday, 01/28/2026, by midnight (23:59, EST)

Announcements

Goals

The goals for this lab assignment are:

  • Understand Supervised Learning

  • Get familiar with Nearest Neighbors

  • Get familiar with Decision Trees

  • Get familiar with Ensemble Methods

  • Get familiar with the Final Paper format

1. Supervised Learning (One Hour)

  • User Guide

  • Read the User Guide

  • Read the related chapters in the three optional textbooks

  • Search online about supervised learning

  • Write in your own words what supervised learning is, in four to five sentences, in your notes.txt file.

  • Add the references, which resources are most helpful for you to understand this concept.

2. Nearest Neighbors (One Hour)

  • Read the guide from 1.6. Nearest Neighbors

  • Example 1: Comparing Nearest Neighbors …​

    1. Download Python source code: plot_nca_classification.py

  • For the example above:

    1. Download the existing code

    2. Set up the coding environment

    3. Run the existing code

    4. Track the execution time and write them down in your notes.txt file.

    5. Take the screenshots of your results after running the code, the command line window with run time.

  • Add the references, which resources are most helpful for you to understand this concept.

  • Write in your own words what 'Nearest Neighbors' is in your notes.txt file in four to five sentences.

3. Decision Trees (One Hour)

  • Read the guide from 1.10. Decision Trees

  • Example 1: Plot the decision surface of decision trees …​

    1. Download Python source code: plot_iris_dtc.py

  • For the example above:

    1. Download the existing code

    2. Set up the coding environment

    3. Run the existing code

    4. Track the execution time and write them down in your notes.txt file.

    5. Take the screenshots of your results after running the code, the command line window with run time.

  • Add the references, which resources are most helpful for you to understand this concept.

  • Write in your own words what 'Decision Trees' is in your notes.txt file in four to five sentences.

4. Ensemble methods (One Hour)

  • Read the guide from 1.11. Ensemble methods

  • Example 1: Plot the decision surfaces of ensembles of trees …​

    1. Download Python source code: plot_forest_iris.py

  • For the example above:

    1. Download the existing code

    2. Set up the coding environment

    3. Run the existing code

    4. Track the execution time and write them down in your notes.txt file.

    5. Take the screenshots of your results after running the code.

  • Add the references, which resources are most helpful for you to understand this concept.

  • Write in your own words what 'Ensemble methods' is, in four to five sentences, in your notes.txt file.

5. Final Paper Examples (Two Hours)

  • Read ten accepted papers from our research lab.

  • Select your top 3 papers of interest.

  • Summarize why you chose each paper in your notes.txt file.

6. Submission Guide

  • Each student only submits one file, lab_2_lastname.zip, including

    1. notes_lab_2_lastname.txt for your notes, including the code run time.

    2. A screenshot folder for all the screenshots files (PNG or JPEG), total size less than 5 M.

7. Notes

  • Submit your zip file for the lab to BlackBoard.

  • Lab assignments will typically be released on Thursday and will be due by midnight on the following Wednesday.