## 🗓️ Schedule (tentative)

</tr></tr></tr></tr>Date | Description | Course Materials | Events | Deadlines |
---|---|---|---|---|

Prepare | Python Tutorial [YouTube] Numpy, Pandas, Matplotlib [notes] [YouTube] | Suggested Readings:- learnpython.org
- The Python Tutorial (official Python documentation)
| ||

Sep 08 | Background, dataset, evaluation, metrics, baseline methods [slides] [colab] [github] | Suggested Readings: | HW 1 release | |

Sep 15 | Correlation-based collaborative filtering [github] | Suggested Readings: | HW 1 due | |

Sep 22 | Quiz 1: implement baseline methods and correlation-based collaborative filtering | InClass quiz via Kaggle (link on BlackBoard) | ||

Sep 29 | ML overview | Suggested Readings: | HW 2 release | |

Oct 06 | Matrix factorization I: ALS | Suggested Readings:- Netflix Update: Try This at Home (first one applied MF in RS)
- Matrix factorization techniques for recommender systems
- Finding Similar Music using Matrix Factorization </li>
- Finding Similar Music using Matrix Factorization </li>
- Matrix factorization techniques for recommender systems
- Matrix completion and low-Rank SVD via fast alternating least squares </ol> </td>
| HW 2 due | |

Oct 13 | Matrix factorization II: SGD | Suggested Readings:- Stochastic Gradient Descent (sklearn documentation)
- Stochastic Gradient Descent Algorithm With Python and NumPy
| ||

Oct 20 | Factorization Meets the Neighborhood | Suggested Readings: | ||

Oct 27 | Case Study: MovieLens | Suggested Readings:- Home Depot Product Search Relevance (Kaggle competition)
| Proj 2 release | Proj 1 due |

Nov 03 | Neural Networks | Suggested Readings:- Chapter 11 in The Elements of Statistical Learning
- Neural Networks and Deep Learning (free online book)
| ||

Nov 10 | Neural collaborative filtering | Suggested Readings: | HW 3 release | |

Nov 17 | Side information | HW 3 due | ||

Nov 24 | Model Averaging | Suggested Readings: | HW 3 due | |

Dec 01 | Quiz 2: Math & Python | InClass quiz | ||

[Lecture Cancelled] | Extra project office hours available during usual lecture time, see Ed. | |||

- | Proj 2 due |