Skip to content

README.md

Latest commit

 

History

History
13 lines (8 loc) · 974 Bytes

README.md

File metadata and controls

13 lines (8 loc) · 974 Bytes

MachineLearning

This is a collection of software modules and scripts that help solve various Machine Learning problems. I will try to focus on problems that are difficult to find in standard library packages.

I will add theory and code for the following problems:

  1. Linear Regression using l1 (lasso) penalty - A hard problem because of its obvious non-differentiability.
  2. Sparse Covariance Estimation for a multivariate Gaussian - Estimation of the covariance matrix of a Gaussian using samples drawn from the (unknown) Gaussian. A seemingly standard problem that is surprisingly hard.
  3. Logistic Regression using l1 (lasso) penalty - self-explanatory.
  4. Implementation of the Baum-Welch algorithm for Hidden Markov Models (HMMs) - Learning HMM parameters when no tagged data is available.

If you would like me to write up MATLAB or Python code for a Machine Learning algorithm, please don't hesitate to reach out at amang@andrew.cmu.edu or aman2304@gmail.com