👨‍💻 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 (40%): Open-book in-class Kaggle session, held approximately mid-semester. Implement SVD recommender system methods.

  • Final In-Class Coding Quiz (45%): Basic Python programming and implementation of recommender systems models, held in the last class 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).