Feb 2017
13 Mon
14 Tue
15 Wed
16 Thu 09:00 AM – 06:00 PM IST
17 Fri 09:00 AM – 06:00 PM IST
18 Sat
19 Sun
Sourav Singh
There is a growing interest in data-driven modelling and statistical learning for NeuroImaging. Datasets for NeuroImaging are also growing in size and complexity.
Nilearn was designed to combat these problems. The Python package provides nicely written methods for preprocessing, analysis and visualization of Neuroimaging results.
The talk aims to introduce people to Nilearn and its workings and how it can make data analysis easy for Neuroscientists. The talk will mostly consist of code-demos to show the various features of nilearn.
The intended audience for the talk are Python programmers who have an understanding of Machine Learning algorithms. A small understanding of Neuroimaging would be a plus.
To understand a few concepts on NeuroImaging, here are the suggested resources-
Brief Introduction on NeuroImaging- http://miykael.github.io/nipype-beginner-s-guide/neuroimaging.html
NIfTI file format- https://nifti.nimh.nih.gov. Give an understanding on the type of files used for NeuroImaging.
What is a Connectome?- https://youtube.com/watch?list=PLCEC78997E3E2DAB4&v=Rm1KLXIDS_Y. This video is for those who wish to have a deep understanding of what is a connectome and not for those who are looking for a quick understanding.
Another video on Connectome by Sebastian Seung for a simpler understanding- https://www.ted.com/talks/sebastian_seung
The talk will take in mind the background of the attendees and make it as simple as possible for attendees to understand NeuroImaging.
The talk will proceed as follows-
If time allows, I will also cover spatial network extraction using ICA(Independent Component Analysis).
I am a student in Computer Science who is interested in the field of Machine Learning, Bioinformatics, Neuroinformatics and linguistics.
I have been a part of Django Girls Pune since two years as a mentor.
I often listen to Western Classical Music, practice Taekwondo and learning to speak Russian fluently during my free time.
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