Implement machine learning algorithms while trying my best to avoid the usage of machine learning libaries such as sklearn, only use numpy, scipy
Contents
- Linear Algebra: Gaussian Elimination and Gram-Schmidt orthogonalization
- K-means: sklearn was used to plot the Elbow method, which is optional
- Polynomial Regression Using Gradient Descent
- GMM: sklearn was used to initialize parameters for simplicity, actually I can use the hand-made K-means, or just use a set of randomly initialized parameters
- PCA of High Dementional Data
Note: encapsulation needed and will be done in the future