🗓️ Schedule (tentative)

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DateDescriptionCourse MaterialsEventsDeadlines
PreparePython Tutorial
[YouTube]

Numpy, Pandas, Matplotlib
[notes] [YouTube]
Suggested Readings:
  1. learnpython.org
  2. The Python Tutorial (official Python documentation)
Sep 08Background, dataset, evaluation, metrics, baseline methods
[slides] [colab] [github]
Suggested Readings:
  1. Wiki: Netflix Prize
  2. Recommender Systems Datasets - UCSD CSE </ol> </td>
HW 1 release
Sep 15Correlation-based collaborative filtering
[github]
Suggested Readings:
  1. K-Nearest Neighbors Algorithm
  2. Cosine Similarity in NLP </ol> </td>
HW 1 due
Sep 22Quiz 1: implement baseline methods and correlation-based collaborative filtering
InClass quiz
via Kaggle (link on BlackBoard)
Sep 29ML overview
Suggested Readings:
  1. Chapters 2-3 in The Elements of Statistical Learning
  2. Linear regression in sklearn </ol> </td>
HW 2 release
Oct 06Matrix factorization I: ALS
Suggested Readings:
  1. Netflix Update: Try This at Home (first one applied MF in RS)
  2. Matrix factorization techniques for recommender systems
  3. Finding Similar Music using Matrix Factorization </li>
  4. Finding Similar Music using Matrix Factorization </li>
  5. Matrix factorization techniques for recommender systems
  6. Matrix completion and low-Rank SVD via fast alternating least squares </ol> </td>
HW 2 due
Oct 13Matrix factorization II: SGD
Suggested Readings:
  1. Stochastic Gradient Descent (sklearn documentation)
  2. Stochastic Gradient Descent Algorithm With Python and NumPy
Oct 20Factorization Meets the Neighborhood
Suggested Readings:
  1. Factorization meets the neighborhood: a multifaceted collaborative filtering model
  2. Smooth neighborhood recommender systems
Oct 27Case Study: MovieLens
Suggested Readings:
  1. Home Depot Product Search Relevance (Kaggle competition)
Proj 2 release Proj 1 due
Nov 03Neural Networks
Suggested Readings:
  1. Chapter 11 in The Elements of Statistical Learning
  2. Neural Networks and Deep Learning (free online book)
Nov 10Neural collaborative filtering
Suggested Readings:
  1. Neural Collaborative Filtering (original paper of NCF)
  2. Deep Learning based Recommender System: A Survey and New Perspectives
  3. TensorFlow Recommenders
HW 3 release
Nov 17Side information
HW 3 due
Nov 24Model Averaging
Suggested Readings:
  1. Chapters 8 and 16 in The Elements of Statistical Learning
  2. Ensemble Models: What Are They and When Should You Use Them?
  3. combo: A Python Toolbox for Machine Learning Model Combination
HW 3 due
Dec 01Quiz 2: Math & Python
InClass quiz
[Lecture Cancelled] Extra project office hours available during usual lecture time, see Ed.
- Proj 2 due