Commercial entities, including search engines, advertisers, media platforms (e.g., Netflix, Amazon), and financial institutions, leverage recommender systems to recommend content, predict customer behavior, ensure compliance, and assess risk. This course provides a comprehensive overview of predictive models for recommender systems, covering content-based and collaborative filtering algorithms, matrix factorization, and deep learning models. Students will gain hands-on experience implementing recommender systems using Python.

👌 What you’ll learn:

  • Understand the fundamental principles underlying various recommender system approaches, including correlation-based collaborative filtering, latent factor models, and neural recommender systems.
  • Gain hands-on experience implementing and analyzing recommender systems for real-world applications using Python, scikit-learn, and TensorFlow.
  • Learn to select and design appropriate models tailored to specific applications.

🏗️ Prerequisites:

  • Calculus & Linear Algebra: Inner products, matrix-vector products, linear regression (OLS).
  • Basic Statistics: Fundamentals of distributions, probabilities, mean, standard deviation, and other core concepts.
  • Python: Familiarity with basic Python syntax and experience with NumPy, pandas, and TensorFlow libraries.
  • (Recommended) Complete a Machine Learning Crash Course (in-person, online, or self-study) or possess equivalent knowledge.
  • ⏲️ Lectures: Thu 12:30PM - 3:15PM
  • 🎒 Lecture/Recitation Location: Mong Man Wai Bldg 710
  • 💻 HW Submission: BlackBoard
  • ⌨️ Colab: Notebook or click Open in Colab

Homepage GitHub Open In Colab

All students welcome: We are happy to have auditors in our lectures.

📋 Reference Textbooks

The following textbooks serve as relevant resources, though none perfectly align with the scope and content of our course.