Each student in this class is asked to do an individual project consisting a term paper and an optional presentation if it can be arranged in the lab.

For your project, you are asked to choose either a machine learning platform or a machine learning library (in any programming ecosystem), and write a technical report to help readers understand your chosen platform/library.

To give you some ideas, here is the top list of machine learning platforms earned "2025 Data Quadrant Awards" from Info~Tech Research Group at:
https://www.infotech.com/software-reviews/categories/machine-learning-platforms

• Microsoft Azure Machine Learning
• Google Cloud Vertex AI
• MathWorks MATLAB
• AWS Machine Learning
• Databricks Data Intelligence Platform
• TensorFlow TFX
• deepsense.ai
• Microsoft Fabric
• DataRobot AI Platform
• Eclipse Deeplarning4j

There are more platforms out there, just to list a few:

• IBM Watson
• OpenML
• Dataiku
• H2O AI Cloud
• Weka
• and lots more

IBM listed following top machine learning libraries with a short description for each at:
https://www.ibm.com/think/topics/machine-learning-libraries

• NumPy
• Tensorflow
• Keras
• PyTorch
• Scikit-learn

Geeks for Geeks listed following popular Python libraries for Machine Learning with a short description for each at:
https://www.geeksforgeeks.org/machine-learning/best-python-libraries-for-machine-learning/

• Numpy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• Pytorch
• Keras
• Tensorfow

You probably can find more platforms/libraries for machine learning. I strongly recommend that you choose one that you are familiar with and preferably you have used before.

Your technical report should include at least the following sections:

• Introduction: provide the general background information and creator information (if there is one) of the platform/library.
• Platform/library functionality: its major features, what it can do; and select at least one functionality to be described in more details, such as its required data input format, any special requirements about its input data, what can be achieved with this functionality and how its achievements/output will be presented, etc.
• Algorithms used by the platform or implemented in the library: list some of the important algorithms used by the platform or implemented in the library, and select at least one representative algorithm to explain in depth.
• Strength of the platform/library: which tasks can be handled really well by this platform/library; any advantages this platform/library has over others.
• Weakness of the platform/library: any significant improvements should be done on this platform/library.
• How to use the platform/library practically: its installation requirements, its set ups, its interface (if there is one) and its organizations.
• Resources if readers want to know more about this platform/library.

The submit deadline of your term paper is 10:00am, 10 April 2026.
You can choose one of the following two ways to submit:

• You can submit a hard copy of your term paper;
• Login to your VIU Learn account, find the CSCI 479 course page, click on the "Assessment" drop-down menu, click on the "Assignments" item, then click on the folder named "Project". Then you can click on the "Add a File" button to browse and upload your term paper.

You will receive one mark for your project. Your project will be marked based on the clarity and logical coherence of explaining your topic in your term paper and (optionally) in your presentation, the depth of your understanding of your chosen topic, and the accuracy of the information conveyed in your project.