https://github.com/jldbc/statistical-learning
Coursework from Big Data (EC3389) -- a course on statistical learning theory with applications in Python
https://github.com/jldbc/statistical-learning
Last synced: about 1 year ago
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Coursework from Big Data (EC3389) -- a course on statistical learning theory with applications in Python
- Host: GitHub
- URL: https://github.com/jldbc/statistical-learning
- Owner: jldbc
- Created: 2016-02-02T01:31:14.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2016-06-02T00:03:10.000Z (about 10 years ago)
- Last Synced: 2025-06-15T16:11:22.066Z (about 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 15.3 MB
- Stars: 2
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Statistical Learning
Coursework from EC3389 - Big Data (Statistical Learning Theory and Applications)
Topics covered include:
* Linear models
* Shrinkage (Ridge, LASSO)
* Resampling Methods
* Classification (Logit, K-Means, KNN)
* Non-linear modeling (Splines, Polynomial Regression)
* Tree-based methods (Decision Trees, Bagging, Random Forests)
* Support Vector Machines
* Unsupervised Learning (PCA, K-Means)
## Assignments
Folder contains programming portion of course assignments
## Lecture Notes
Contains ipython notebooks from applied portion of the course
##Final Project
Final project was a classification task, predicting whether URLs are malicious or benign. Final_Writeup.pdf is LaTeX compiled pdf of final submission, Malicious_URLs.ipynb is the code for the project.