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  • ES335 - Machine Learning
    • Recent Assignments (Fall 2024)
    • Template Assignments
  • ES654 - Machine Learning
  • For Instructors
    • Using These Assignments
    • Assignment Features
  • Contributing & Feedback

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Assignments

Practical machine learning assignments with real-world applications

ES335 - Machine Learning

Recent Assignments (Fall 2024)

Assignment 1 - Fall 2024
Latest machine learning assignment covering fundamental concepts and implementations.

View Assignment GitHub Repository

Assignment 2 - Fall 2024
Advanced machine learning concepts with practical implementation challenges.

View Assignment GitHub Repository

Template Assignments

These are template repositories used for GitHub Classroom:

Assignment 1 Template
Foundational machine learning concepts and basic algorithm implementations.

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Assignment 2 Template
Intermediate machine learning techniques and model evaluation methods.

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Assignment 3
Advanced machine learning applications and real-world problem solving.

View Assignment GitHub Repository


ES654 - Machine Learning

Historical assignments focusing on machine learning concepts and applications:

ES654 2023 Template
Latest template for machine learning concepts and algorithms.

View Template GitHub Repository

ES654 2022 Code
Machine learning implementations and algorithmic approaches (2022).

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ES654 2021 Code
Machine learning techniques and practical implementations (2021).

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ES654 2020 Code
Foundational machine learning concepts and algorithmic implementations (2020).

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For Instructors

Using These Assignments

  • Template Repositories: Click “Use Template” to create new assignments for your courses
  • GitHub Classroom: These repositories are designed to work seamlessly with GitHub Classroom
  • Customization: All assignments can be modified to fit your specific course requirements
  • Auto-grading: Many assignments include GitHub Actions for automated testing

Assignment Features

  • Real-world Datasets: Assignments use practical datasets from various domains
  • Progressive Difficulty: Structured to build skills incrementally
  • Implementation Focus: Emphasis on coding ML algorithms from scratch
  • Evaluation Metrics: Comprehensive assessment criteria and rubrics
  • Documentation: Detailed instructions and starter code provided
Pro Tip for Students

Start early with these assignments! They require both theoretical understanding and practical implementation skills. Use the course materials (slides, tutorials, notebooks) as references while working through the problems.

Contributing & Feedback

Found an issue with an assignment or have suggestions for improvement?

  • Report Issues: Create a GitHub issue for assignment-related feedback
  • Contribute: Submit pull requests with improvements or additional assignments
  • Discussion: Join the conversation about assignment design and pedagogy

All assignments are developed for educational use and are continuously updated based on student feedback and learning outcomes.

ML Resources: From Code to Model
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