The evaluation is based on the successful completion of a course project based on techniques covered in class. Students are required to submit an initial report containing preliminary proposal before the course drop deadline (10%), give 20-minute oral presentation (20%) and submit the final report (30%). There will be exams (40%). In last two years, 16 students took similar courses and the grade distribution is as follows: 1 A, 8 A-, 5 B+, 2 B, 1 C.
Computational Neuroanatomy: The Methods. This is a preprint of book I am currently writing and it will be freely available to students in the dropbox.
statistical issues in neuroimage processing and
be addressed. The focus of the course is on the
computational aspect of
various statisical procedures used in neuroimages.
MATLAB will be used as a language of
instruction although students can do homework and
project in any
of their choice. The following topics will be covered:
Introduction to statistical computing in MATLAB, General
linear models and random field theory.
Week 2: Smoothness of fields, Type-I error. multiple comparision correction using random field theory.
Week 3: Machine learning. Support vector machines (SVM).
Week 4: Type-II error in correlated images. Sample size computation. Power analysis.
Week 5-6: Hilbert space methods and Karhunen-Loeve expansion in random fields.
Week 7-8: Linear models: general linaer models, mixed and fixed effect models.
Week 9-10: More multiple comparisions using FDR and permutation tests. Nonparametric test procedures.
Week 11: Time series analysis.
Week 12-13: Multivariate analysis.
Week 14: Student presentation.
Various applications will be covered in connection with these topics. Some topics overlap with the topics covered in the previous course I taught in 2009 and 2010.