Machine Learning Introduction: String Classification

Machine learning means different things to different people, and there is no general agreed upon core set of algorithms that must be learned. In this class we will therefore not focus so much on specific algorithms or machine learning models, but rather give an introduction to the overall approach to using machine learning in bioinformatics, as we see it. To us, the core of machine learning boils down to three things: 1) Building computer models to capture some desired structure of the data you are working on, 2) training such models on existing data to optimise them as well as we can, and 3) use them to make predictions on new data. In these lecture notes we start with some toy examples illustrating these steps. Later you will see a concrete example of this when building a gene finder using a hidden Markov model. At the end of the class you will see algorithms that do not quite follow the framework in these notes, just to see that there are other approaches.