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UTK Notes


Homework #2: Linear and Logistic Regression

Summary

This second homework assignment is designed to guide you through the fundamental concepts of Linear and Logistic Regression. You will learn how to implement these algorithms from scratch, visualize the results, and understand the relationship between parameters and model performance. The exercises will help you develop a solid understanding of regression techniques, which are essential in many real-world applications.

Key Objectives

  • Linear Regression:
    • Understand the theoretical background of linear regression.
    • Implement the cost function for linear regression.
    • Visualize the linear fit and analyze the impact of different parameters.
  • Logistic Regression:
    • Grasp the theoretical foundations of logistic regression.
    • Implement the sigmoid function and cost function for logistic regression.
    • Optimize model parameters and visualize the decision boundary.
    • Evaluate model performance using confusion matrices and classification reports.

Instructions:

  • Python and Anaconda Installation: Ensure you have Python and Anaconda installed on your computer. Anaconda is important as it includes Jupyter Notebook, which you’ll need to complete this homework.
  • Getting Started: Start the homework as soon as possible since it is due by 9/8. Beginning early will give you ample time to understand and implement the concepts.
  • Code and Guidance: This homework provides code templates and detailed instructions to help you implement linear and logistic regression models. Future assignments will build on these concepts, gradually reducing guidance to help you become more independent
  • Downloading the Homework: Download the homework file from the provided link. This file contains all the instructions and tasks you need to complete.
  • Academic Integrity: No code sharing or cheating is allowed. This includes using any large language models (LLMs) such as ChatGPT, Claude, Grok, or similar tools to generate or complete your code. Violations will result in disciplinary actions.
  • Submission:
    • Complete the notebook, ensuring that all code cells run correctly.
    • Save your work as both a .ipynb file and a PDF file.
    • Submit both files through the Canvas submission link by the due date.

Notes:

  • Start Early: This assignment covers essential concepts that are foundational for your understanding of machine learning. Starting early will allow you time to digest the material and seek help if needed.
  • Resources: Use the tutorials and documentation links provided in the homework for additional support. Refer to these resources to understand the theoretical concepts and Python libraries used in the tasks.

Homework Download