This class will cover advanced machine learning topics. The focus of the class will be graphical models and kernel methods, which are currently the major paradigms for building advanced and sophisticated machine learning models for complex real world problems especially for bioinformatics.
Graphical models provide a unified view for a wide range of problems with a very large number of attributes and huge datasets, where we want to obtain a coherent global conclusion from local information. Concepts and algorithms from graphical models enable efficient inference, decision-making and learning in a variety of problems including artificial intelligence and computational biology.
Kernel methods provide a general framework for extending algorithms designed for finding linear relations and patterns to nonlinear cases. Kernel methods approach the problem by mapping the data into a high dimensional feature space; and in that space, a variety of methods can be used to find relations and patterns in the data. Since the mapping can be quite general (eg., not necessarily linear), the relations found in this way are accordingly very general, and the type of data kernel methods can be applied to is also very general and we will apply them for sequence data and graph data in bioinformatics.
This graduate-level class will provide you with a strong foundation for both applying machine learning to biological real world problems and for addressing core research topics in machine learning.
Other useful books:
Syllabus and Schedule
|Lecture &Topics||Readings & Useful Links||Handouts||Assignments|
|Introduction and probability Background||Slides|
|Introduction to Bayesain Networks||Slides|
There are many software packages available that can greatly simplify the use of graphical models. Here are a few examples: