Duration: 13 hours
We are already at the last week of course material! Get ready for another dense math week. Last week, we learned about Recommendation Systems. We used a Neighborhood Method of Collaborative Filtering, utilizing similarity measures. Latent Factor Models, including the popular Matrix Factorization (MF), can also be used for Collaborative Filtering. A 1999 publication in Nature made Non-negative Matrix Factorization extremely popular. MF has many applications, including image analysis, text mining/topic modeling, Recommender systems, audio signal separation, analytic chemistry, and gene expression analysis. This week, we focus on Singular Value Decomposition, Non-negative Matrix Factorization, and Approximation methods. We have reading, a quiz, and a Kaggle mini-project utilizing matrix factorization to categorize news articles. Next week is the due date for your final course project. Keep running experiments and working on the primary analysis for your final project. Ideally, it would be excellent to finish experimenting and iterate with your models this week so that next week, you can focus on preparing your final project deliverables.