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Python辅导 | GR5293 Applied Machine Learning for Image Analysis

这个Assignment是使用python对图片进行机器学习分析
GR5293 Applied Machine Learning for Image Analysis
Assignment #3
Instructor: Xiaofu He
Fall 2019
Department of Statistics & Data Science Institute
Columbia University
Goal
Implement SVM or other machine learning
methods for brain state classification
(classification accuracy can be based on the
whole brain or region of interest from the fMRI
data).
Citateion’
a) Download data from coursework. For sub-01/ses-test, load in data
and labels.
b) Preprocessing: realignment (optional); create a brain mask to
extract brain regions (i.e., exclude non-brain voxels) using a single
threshold and save the mask.
c) Feature selection: extract brain regions using the brain mask, i.e.,
exclude background (non-brain voxels) , and/or use dimension
reduction (optional) such as PCA.
d) Classification: SVM or other method
e) K-fold cross-validation (e.g., K=4, 8…)
f) Play around the parameters (e.g.,. parameters (brain mask
threshold, SVM parameters, number of PCA components
(optional)). Submit the best accuracy (>=90%) and record all
parameters.
Repeat step (a) to (f) using sub-01/ses-retest data
Requirement
For example: 5-fold cross-validation
From http://cs231n.github.io/classification/
1. Realign sub-01/ses-test and sub-01/ses-retest
together (optional), so that you can train your
model using sub-01/ses-test data only and test
your model using sub-01/ses-retest. You will
receive extra 5% point if you can do so and
achieve descent accuracy ( > 70%) (It will be
challenging)
Extra credit
Submission (Option 1 Matlab)
• One Matlab file, i.e., combine all steps into one script, which must be runnable
(we won’t debug for you).
– UNI_Name_Assignment3.m
• A readme file (UNI_Name_Assignment3.README)
– How to run your code? Any variable needs to be changed before we run your
code (e.g., variable for directory)?
– Briefly discuss the limitation(s). How can you improve it?
– Describe the results of the experiment in your own words. Compare the
results of two approaches (e.g., SVM only vs PCA+SVM) and briefly discuss
why one works better/worse than the other.
• DO NOT submit the dataset
• Compress all files and folder into a single compressed file with
UNI_Name_Assignment3.zip/.rar as its name
Submission (Option 2 Python)
• One python file (i.e., py), i.e., combine all steps into one script, which must be
runnable (we won’t debug for you).
– UNI_Name_Assignment3.py
• A readme file (UNI_Name_Assignment3.README) requirements see previous
slide
• DO NOT submit the dataset
• Compress all files and folder into a single compressed file with
UNI_Name_Assignment3.zip/.rar as its name
Submit to the coursework, due on
11/20/2019 (11:59PM)
Hints
For Matlab
• use the following SPM functions to read your
fMRI data
– spm_vol: Get header information for images
– spm_read_vols: Read in entire image volumes
e.g.,
imgHeader = spm_vol(imgFile);
imgData = spm_read_vols(imgHeader);
• feel free to use exiting toolboxes that simplify
the work for you
For python
– scipy.io for loading .mat data file if you processed
the data in Matlab or use a Python package to load
nifty data
– feel free to use other packages to make your life
easier


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