👨‍💻 Coursework:

  • Homework Assignments (15%): Three homework assignments will be given, each requiring submission of a well-documented Jupyter Notebook.

    • Homework 1 (5%): Implementing k-fold cross-validation
    • Homework 2 (5%): Practicing ALS-related algorithms
    • Homework 3 (5%): Prototyping neural networks in recommender systems using TensorFlow
  • In-Class Kaggle Session (50%): Open-book in-class Kaggle session.
    • Lab Attendance (5%): Attendance in the lab sessions is mandatory. You will receive 5% credit if you attend all lab sessions.
    • Kaggle Competition (45%): Implement SVD recommender system methods.
  • Final In-Class Quiz (coding and/or exercises) (35%): Basic Python programming and implementation of recommender systems models (in the final lecture of the semester).

📝 Academic Honesty: Our course places very high importance on honesty in coursework submitted by students, and adopts a policy of zero tolerance on academic dishonesty.

📢 Late Submission: Homework and submissions are submitted via BlackBoard. We will penalize 10% credit per 12 hours for late submissions (up to a maximum of 50% penalization).