Graduate machine learning and data processing coursework completed at Stevens Institute of Technology during Spring 2026.
This repository contains lecture notes, reports, and project work spanning time series analysis, ensemble methods, recommender systems, logistic regression, and feature selection. The work is centered on Python-based implementations and quantitative evaluation against real-world datasets.
- Lecture summaries and supporting course material organized by week
- Homework and project submissions with technical writeups and supporting code
- Applied modeling work in forecasting, classification, recommendation, and feature selection
- Time series modeling with autoregressive and ARIMA methods
- Yahoo Music recommender system project with iterative model tuning and evaluation
- Logistic regression and feature selection exercises on real datasets
- Ensemble-based modeling and data analysis workflows documented in LaTeX reports
| Week | Date | Topics |
|---|---|---|
| Week 01 | 01-23 | Course Introduction |
| Week 02 | 01-30 | XGBoost, Correlation, Autoregressive Models, Time Series |
| Week 03 | 02-06 | AR Model Selection, ARMA Modeling |
| Week 04 | 02-13 | Market Basket Analysis, ARIMA, Transition to Recommender Systems |
| Week 05 | 02-20 | Recommender System Theory |
| Week 06 | 02-27 | Yahoo Music Recommender Project, Data Structures |
| Week 07 | 03-06 | Logistic Regression |
| Week 08 | 03-13 | Feature Selection, Best Subset GLM |
- Python 3 (standard library +
collections.defaultdict) - LaTeX (pdflatex with
amsmath,booktabs,listings,tocloft,hyperref) - Git LFS for large data files (
trainItem2.txt,trainIdx2_matrix.txt,test.csv,training.csv)