This repo covers state-of-the-art techniques for pattern recognition, as they are typically employed in a number of practical applications.
The implementation is done from scratch-math level.
In more detail, the following concepts are covered :
- Decision theory and the Bayesian approach to classification.
- Maximum likelihood parameter estimation and the expectation maximization algorithm.
- Nearest neighbor based classifier.
- Bayesian networks.
- Linear and non-linear classifiers.
- Neural networks.
- Support vector machines.
- Decision trees.
- Markov chains and hidden Markov models.
- Classifier combination.
- Feature selection based on various approaches.
- Data transforms and feature vector dimensionality reduction.
- Basic concepts in clustering.
- Basic clustering algorithms, including K-means, sequential, and agglomerative clustering.
For a more practical approach, including R, Python, SaS and numerous frameworks/libraries, feel free to check my ML repo.