General introduction & organization¶
Peer Herholz (he/him)
Postdoctoral researcher - NeuroDataScience lab at MNI/McGill, UNIQUE
Member - BIDS, ReproNim, Brainhack, Neuromod, OHBM SEA-SIG
@peerherholz
Aim(s) of this section¶
get a basic idea of what will happen
answer questions
address setup problems
Outline for this section¶
What is happening?
Who are you?
Who are we?
The framework and setup
General questions
What is happening?¶
Objectives¶
Gain skills
Learn about machine and deep learning methods and how to apply them with a focus on neuroscientific data
Familiarize yourself with different model types & analysis pipelines so that you can critically evaluate them
Know important limitations & biases present in these methods
What is happening?¶
Objectives¶
Share
Bring everything you’ve learning to your home institution and/or lab and beyond
What is happening?¶
Objectives¶
Get involved
Get to know what’s going on in the open science/source-python-machine/deep learning-neuroscience-world
Become an active part of this cool community & support it going further
What is happening?¶
Your role¶
ask questions
think quick and towards the “bigger picture”
further familiarize yourself with the machine/deep learning-python-world
start thinking about how you could apply/integrate the techniques introduced here into your own research workflow
have a great time (that’s actually more on us than you)
give us feedback and help improve the materials
What is happening?¶
Schedule¶
Welcome (9 AM - 9:30 AM)
Time slot |
Topic |
|---|---|
9 AM - 9:15 AM |
General hello, introduction round and organization (9 AM - 9:15 AM) |
9:15 AM - 9:30 AM |
Models, AI and all other buzz words (9:15 AM - 9:30 AM) |
What is happening?¶
Schedule¶
The content I - theoretical background (9:30 AM - 12 PM)
Time slot |
Topic |
|---|---|
9:30 AM - 10 AM |
“Classic” machine learning - supervised or unsupervised, model types |
10 AM - 10:15 AM |
yoga/dance break |
10:15 AM - 10:45 AM |
“Classic” machine learning - model evaluation & cross-validation |
10:45 AM - 11:15 AM |
“Classic” machine learning - model tuning & biases |
11:15 AM - 11:30 AM |
yoga/dance break |
11:30 AM - 12 PM |
Deep learning - basics & architectures |
What is happening?¶
Schedule¶
The content I - theoretical background (1 PM - 2:15 PM)
Time slot |
Topic |
|---|---|
1 PM - 1:30 PM |
Deep learning - how to build & train a neural network |
1:30 PM - 2 PM |
Deep learning - model tuning & biases |
2 PM - 2:15 PM |
yoga/dance break |
What is happening?¶
Schedule¶
The content II - hands-on (2:15 PM - 4 PM)
Time slot |
Topic |
|---|---|
2:15 PM - 2:45 PM |
Dataset blitz |
2:45 PM - 3:45 PM |
Free hacking |
3:45 PM - 4 PM |
Lessons learned, Q&A |
Who are you?¶
your name
your background
your programming/ML/DL experience
your favorite band/artist
if you could be any type of vacation: which one?
(point 2-4 are not intended to “put you on the spot”, but to get a better idea of your previous training/experience so that we have the chance to tailor the workshop contents better to your needs)
Who are we?¶
a little something about us…
Who are we?¶
Characteristic/Person |
José C. García Alanis |
Peer Herholz |
|---|---|---|
Picture |
||
Affiliation |
Child and Adolescent Psychology, Philipps-Universität Marburg |
Research affiliate MNI/McGill & McGovern Institute/MIT, cand. habilitation Goethe-University Frankfurt |
Background |
Psychology |
Neuropsychology/Neuroscience |
Research |
Human decision making, stistical modelling, EEG |
Cognitive & computational auditory neuroscience |
Likes |
Peer |
José |
Dislikes |
Peer |
José |
Contact |
|
|
The framework and setup¶
there are 4 different ways to participate in this workshop and utilize the materials
https://peerherholz.github.io/ML-DL_workshop_SynAGE/setup.html
General Questions¶
