The primary objective of this course is to discuss what NeuroImaging methods are available to study the brain. The focus of the course will be on modern tools capable of whole-brain imaging (mostly MRI), but we will also discuss non-MRI techniques as well. As part of the term project, students will be asked to propose novel acquisition and/or analysis method that is likely to facilitate our ability to understand the brain.
Introduce students the fundamental concepts and intuition behind modern machine learning techniques and algorithms, beginning with topics such as perceptron to more recent topics such as boosting, support vector machines and Bayesian networks. Statistical inference will be the foundation for most of the algorithms covered in the course.
This course will discuss the key differences in architecture and algorithms between conventional information processing systems (e.g. von Neumann machines) and biological brains. Subsequently, we will try to come up with the scaffold of a basic design for a non-von Neumann type of brain-like information processing system.
This course covers the theory and application of neural networks. In particular, lectures explore the structure and function of neural networks and their learning and generalization. Also, various models of neural networks and their applications are illustrated.