Different algorithms
In this course we will build our machine learning approach on a statistical foundation, with elements
from data analysis, stochastic processes etc before we proceed with the following machine learning algorithms
- Linear regression and its variants
- Decision tree algorithms, from simpler to more complex ones
- Nearest neighbors models
- Bayesian statistics
- Support vector machines and finally various variants of
- Artifical neural networks
Before we proceed however, there are several practicalities with data analysis and software tools we would
like to present. These tools will help us in our understanding of various machine learning algorithms.
Our emphasis here is on understanding the mathematical aspects of different algorithms, however, where possible
we will emphasize the importance of using available software.