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<!DOCTYPE html>
<html>
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<title>Machine Learning HW6 (2017, Fall)</title>
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# Assignment #6 Unsupervised learning & dimension reduction
<hr>
### Announcement
* Kaggle Deadline: 2018/1/11 11:59 P.M. (GMT+8)
* Github Deadline: 2018/1/12 11:59 P.M. (GMT+8)
* TA 會於 2018/1/5 00:00 A.M. (GMT+8) 釋出範例程式碼，亦為超過 Kaggle simple baseline 的加分截止期限
<hr>
### Link
* 投影片連結 <a href="https://docs.google.com/presentation/d/1v2aJnjqplnQ5YSprp6IXbWM_VPavtolqpgbGWM4HidY" target="_blank"><i class="fa fa-slideshare"></i></a>
* Kaggle 連結 <a href="https://www.kaggle.com/t/3dea5a5172844c9186545a06dcdf0a85" target="_blank"><i class="fa fa-trophy"></i></a>
* TA hours 投影片連結 <a href="https://docs.google.com/presentation/d/1TBFjWJcOecIt29vZLUKOYm5me7ZmfeOl9tYh3xWV9Kw" target="_blank"><i class="fa fa-trophy"></i></a>
* code 遲交表單 <a href="https://goo.gl/forms/U739TuuKJE3QDdWb2" target="_blank"><i class="fa fa-file-text"></i></a>
* github 遲交表單 <a href="https://goo.gl/forms/ulB0FqGngd8cmvjf2" target="_blank"><i class="fa fa-file-text"></i></a>
* 小老師申請表單 <a href="https://goo.gl/forms/xSn2IjAaXMbouE733" target="_blank"><i class="fa fa-file-text"></i></a>
* report template <a href="https://docs.google.com/document/d/13c6RqKvcYdSBMxq4yFljUUDnj5fXmv9cGa01yfFwcFU/edit?usp=sharing" target="_blank"><i class="fa fa-file-text"></i></a>
<hr>
### <a href="https://github.com/ntumlta/2017fall-ml-hw6/blob/master/HW6.ipynb" target="_blank">Simple baseline sample code</a>
<hr>
### Report
#### Part A: PCA of colored faces
* 作業要求: <br />
(.5%) 請畫出所有臉的平均。<br />
(.5%) 請畫出前四個 Eigenfaces，也就是對應到前四大 Eigenvalues 的 Eigenvectors。<br />
(.5%) 請從數據集中挑出任意四個圖片，並用前四大 Eigenfaces 進行 reconstruction，並畫出結果。<br />
(.5%) 請寫出前四大 Eigenfaces 各自所佔的比重 (explained variance ratio)，請四捨五入到小數點後一位。
#### Part B: Visualization of Chinese word embedding
* 作業要求: <br />
(.5%) 請說明你用哪一個 word2vec 套件，並針對你有調整的參數說明那個參數的意義。<br />
(.5%) 請在 Report 上放上你 visualization 的結果。<br />
(.5%) 請討論你從 visualization 的結果觀察到什麼。
#### Part C: Image clustering
* 作業要求: <br />
(.5%) 請比較至少兩種不同的 feature extraction 及其結果。<br />
(.5%) 預測 visualization.npy 中的 label，在二維平面上視覺化 label 的分佈。<br />
(.5%) visualization.npy 中前 5000 個 images 來自 dataset A，後 5000 個 images 來自 dataset B。請根據這個資訊，在二維平面上視覺化 label 的分佈，接著比較和自己預測的 label 之間有何不同。<br />
(visualization.npy 將在 Kaggle deadline 之後公布在 Kaggle 上)
<hr>
### Rules
* Basic : No extra dataset. No plagiarism.
* Allowed toolkits for "PCA of colored faces":
+ NumPy 1.13+
+ scikit-image 0.13+
* Allowed toolkits for "Visualization of Chinese word embedding":
+ you can use anything
* Allowed toolkits for "Image clustering":
+ python 3.5+
+ tensorflow 1.3
+ keras 2.0.8
+ pytorch 0.2.0
+ scikit-image 0.13+
+ pillow 4.3.0
+ scikit-learn 0.19+
+ pandas 0.20+
+ numpy
+ scipy
+ matplotlib
+ h5py
+ Python Standard Lib
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<center><i class="fa fa-github"></i></a> Posted by: <a href="https://github.com/ntumlta/" target="_blank">ntumlta</a> </center>
<center><i class="fa fa-envelope"></i> Contact information: <a href="mailto:"> ntu.mlta@gmail.com </a>.</center>
<center><i class="fa fa-mortar-board"></i> Course information: <a href="http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html", target="_blank">Machine Learning (2017, Fall) @ National Taiwan University</a>.</center>
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