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jyfeather / ds_seeking.md
Last active March 31, 2017 15:52
Yan's Job Seeking Preparation

statistics

machine learning

  • overleaf, machine learning cheat sheet
  • review derivation
  • bias-variance
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jyfeather / temp
Created February 23, 2017 22:29
https://www.cs.cmu.edu/~epxing/Class/10708-14/lecture.html
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jyfeather / minimum_norm.md
Created June 6, 2016 21:58 
% Finding the minimum of a submodular function using Wolfe's min norm point
% algorithm [Fujishige '91]
% Implementation by Andreas Krause (krausea@gmail.com)
%
% function A = sfo_min_norm_point(F,V, opt)
% F: Submodular function
% V: index set
% opt (optional): option struct of parameters, referencing:
%
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jyfeather / script_EE546_FP
Created June 1, 2016 18:01
Our project is prognostics of Alzheimer's Disease using multimodality data. The purpose is that we want to predict when the AD will happen to the subject, and multimodality data means that we use different data sources including fMRI, PET-scan, demographcis and etc., some of them are static and some of them are time-series.
Thus the input of our project is like this, X matrix. Here X is a spatial temporal matrix. We have P features, and for each subject, each feature is measured at four different time points, baseline, after 12 month, after 24 month, and after 36 month. And we use a linear model to predict the time of AD onset, y, which is our output. The parameter W is in matrix form, since it is multiplied by the spatial temporal data of each subject.
This is our formulation. The first two l2 norm terms are to measure the goodness of fit, especially the second term, it is for censored samples, we prefer that the predicted y should at least be greater than the given y. And the penalty term, nuclear norm i
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jyfeather / script_12_02_2015.md
Created December 2, 2015 07:19

slide 1

Good morning, everyone. My name is Yan Jin, I am a phd student from industrial engineering department at university of washington. Today I will introduce our project, we call it insight project. My advisor, Dr. Shuai Huang and Dr. Guan Wang from LinkdedIn, and I are working on insight project together.

slide 2

The insight project is web-based, this is a screen shot of it. It is from a old version, because the current version is under development, I could not get screenshot of newer one.

slide 3

This is the outline for today. First, I will talk about what this project can do, and then how to do this work, so basically it has two main functionalities, one is from given dataset to answers that users might be interested in, and the other is from natural language questions that users ask to answers, and some tenical details in the end.

slide 4

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jyfeather / script_12_01_2015.md
Created November 30, 2015 20:15
for supply chain course INDE 570

slide 1

Good morning, my name is Yan, and this is my teammate Juan. We will talk about this paper, name is emerging market characteristics and supply network adjustments in internationalizing food supply chains.

slide 2

This is the outline. First is the introduction to the backgrounds of the problem, then is their framwork to show how they consider this problem. And then Juan will talk the analysis and conclusion parts.

slide 3

The big background is globalization in food supply network. Globalization makes international food supplier have more oppotunities, for example, Starbucks now is very welcome in China, even Chinese people do not always drink coffee. However, Globalization also makes the design of food supply network more complicated, because some environments in developed countries are different from in developing counties, like the availability of infrastructure and resource.

In this paper, they use EM (emerging markets) to represent developing countries, like BRIC economies, so which

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jyfeather / script_11_19_2015.md
Created November 19, 2015 06:35

slide 1

Now I will talk about the current progress of our project.

slide 2

These are two objectives of this project. First, we applied SICE method on new dataset, I call it NACC dataset, because it is from prof. Zhou, SICE is used to construct brain connectivity network based on resting fMRI data. After network is built, we compute several network statistics and do the reliability and reproducibility analysis, and compare the result with threshold method, to see if there is advantage for SICE in terms of reliability and reproducibility.

However, this work looks not so nice since we just applied existing methods and made a comparison, we want to improve more, especially in methodology part. And I have another question, what could be our target journal to submit? I will lease these in the end.

slide 3

This is a big picture of brain network analysis in general. To guide our study, we follow this flow chart to do the analysis. There are five components in this big picture, dataset, ..., connection

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jyfeather / networkData.csv
Created November 8, 2015 22:23
Directed Network in D3 for Dr. Huang
source target value
1 2 1
1 3 1
1 4 1
1 5 1
1 6 1
1 7 1
1 8 1
1 9 1
1 10 1
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jyfeather / data.mat
Created November 8, 2015 19:33
connectivity plot for Dr. Huang
4d41 544c 4142 2035 2e30 204d 4154 2d66
696c 652c 2050 6c61 7466 6f72 6d3a 204d
4143 4936 342c 2043 7265 6174 6564 206f
6e3a 2053 6174 204f 6374 2032 3420 3133
3a35 323a 3434 2032 3031 3520 2020 2020
2020 2020 2020 2020 2020 2020 2020 2020
2020 2020 2020 2020 2020 2020 2020 2020
2020 2020 0000 0000 0000 0000 0001 494d
0f00 0000 ba00 0000 789c ed57 6d0a 8020
0c9d 44d1 affe 7893 eed6 1d3a 52b7 3325
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jyfeather / MRI_AD.csv
Created September 29, 2015 03:16
sample data for BrainConnectivity.cc
Precentral_L Precentral_R Frontal_Sup_L Frontal_Sup_R Frontal_Sup_Orb_L Frontal_Sup_Orb_R Frontal_Mid_L Frontal_Mid_R Frontal_Mid_Orb_L Frontal_Mid_Orb_R Frontal_Inf_Oper_L Frontal_Inf_Oper_R Frontal_Inf_Tri_L Frontal_Inf_Tri_R Frontal_Inf_Orb_L Frontal_Inf_Orb_R Rolandic_Oper_L Rolandic_Oper_R Supp_Motor_Area_L Supp_Motor_Area_R Olfactory_L Olfactory_R Frontal_Sup_Medial_L Frontal_Sup_Medial_R Frontal_Mid_Orb_L2 Frontal_Mid_Orb_R2 Rectus_L Rectus_R Insula_L Insula_R Cingulum_Ant_L Cingulum_Ant_R Cingulum_Mid_L Cingulum_Mid_R Cingulum_Post_L Cingulum_Post_R Hippocampus_L Hippocampus_R ParaHippocampal_L ParaHippocampal_R Amygdala_L Amygdala_R Calcarine_L Calcarine_R Cuneus_L Cuneus_R Lingual_L Lingual_R Occipital_Sup_L Occipital_Sup_R Occipital_Mid_L Occipital_Mid_R Occipital_Inf_L Occipital_Inf_R Fusiform_L Fusiform_R Postcentral_L Postcentral_R Parietal_Sup_L Parietal_Sup_R Parietal_Inf_L