Mohammad Imran
King Faisal University
Saudi Arabia
Title: Machine learning approaches for biomedical and biometric data
Biography
Biography: Mohammad Imran
Abstract
The aim of this talk is to apply a particular category of machine learning and pattern recognition algorithms, namely the supervised and unsupervised methods, to both biomedical and biometric images/data. This presentation specifically focuses on supervised learning methods. Both methodological and practical aspects are described in this presentation. The presentation is in two parts. In the first part, I will introduce data preparation concepts involved in preprocessing. After a quick overview, I will give an overview dimensions /features, curse of dimensionality, understanding data impurities like missing data and outliers, data transformations, scaling, estimation, normalization, smoothening, etc. In the second part of the presentation, I will discuss issues and challenges specific to 1. Supervised and unsupervised learning, 2. Statistical learning theory, 3. Errors and noise, 4. Bias and variance tradeoff, 5. Theory of generalization, 6. Training vs. testing, and 7. Over-fitting vs. under-fitting. A whole summarization of learning concept and its applications will be done. During the course of the presentation, I will attempt to survey some results on biometric and biomedical data. Finally, future challenges will be discussed.