Machine-Learning-and-Optimization-Algorithms

This repository aim to provide a collection of work done on the implementation of Algorithms relevant to Optimization/Machine Learning.

1. Implementing Optimization Schemes: e.g. Gradient (or Steepest) Descent and its variants, Newton’s method, Quasi-Newton methods like BFGS, etc., for finding the optimal value of a given function and converging to it in the least number of Iterations.
2. Implementing Machine Learning algorithms used for classification, regression and clustering applications.
3. Implementation of algorithms used in linear, nonlinear, stochastic and discrete optimization.