**A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks.**

Machine Learning and Pattern Classification

Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]
An Introduction to simple linear supervised classification using scikit-learn [IPython nb]

About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [IPython nb]

Projection
- Component Analyses
  - Linear Transformation
    - Principal Component Analysis (PCA) [IPython nb]
    - Linear Discriminant Analysis (LDA) [IPython nb]
Feature Selection
- Sequential Feature Selection Algorithms [IPython nb]

Parametric Techniques
- Introduction to the Maximum Likelihood Estimate (MLE) [IPython nb]
- How to calculate Maximum Likelihood Estimates (MLE) for different distributions [IPython nb]
Non-Parametric Techniques
- Kernel density estimation via the Parzen-window technique [IPython nb]
- The K-Nearest Neighbor (KNN) technique
Regression Analysis
- Linear Regression
  - Least-Squares fit [IPython nb]
- Non-Linear Regression

Supervised Learning
- Parametric Techniques
  - Univariate Normal Density
    - Ex1: 2-classes, equal variances, equal priors [IPython nb]
    - Ex2: 2-classes, different variances, equal priors [IPython nb]
    - Ex3: 2-classes, equal variances, different priors [IPython nb]
    - Ex4: 2-classes, different variances, different priors, loss function [IPython nb]
    - Ex5: 2-classes, different variances, equal priors, loss function, cauchy distr. [IPython nb]
  - Multivariate Normal Density
    - Ex5: 2-classes, different variances, equal priors, loss function [IPython nb]
    - Ex7: 2-classes, equal variances, equal priors [IPython nb]
- Non-Parametric Techniques
Unsupervised Learning