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<!DOCTYPE html>
<html>
<link rel="shortcut icon" href="favicon.ico">
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<title>Machine Learning (2017, Fall)</title>
<xmp theme="cerulean" style="display:none;">
# Assignment 2 - Income Prediction
Let's do the BINARY CLASSIFICATION !!!
<hr>
### FAQ
* Q1.請問我可以在github上傳自己的weight並在shell script中使用嗎？
<br>可以，要是同學擔心自己在hw2_best.sh中使用的方法，training的時間可能會超過時限，可以上傳weight，我們到時候clone下來只要確定同學的code可以infer就好。
* Q2.請問Report.pdf名稱與HW1規定不一致，是大小寫都可以用嗎？
<br>請同學依照HW2中投影片的名稱繳交。
<hr>
### Announcement
* Kaggle Deadline: 2017/10/26 11:59 P.M. (GMT+8)
* Github Deadline: 2017/10/27 11:59 P.M. (GMT+8)
* TA會於10/20釋出範例程式碼，亦為超過 Kaggle simple baseline 的加分截止期限
* (10/13) 整理同學課堂問題，請見最上面的FAQ
* (10/17) 新增hw2_best.sh可以使用的套件，請見下方Rules
* (10/20) 上傳samplecode，請見Requirements裡的連結，或是Link的Github連結。
* (10/27) 上傳answer，請見Dataset部分
<hr>
### Link
* 投影片連結 <a href="https://docs.google.com/presentation/d/1qMxjDGkS6fVY0LtMh_VJFhl9QNk0GtiEJegxw3Eq3Ok/edit?usp=sharing" target="_blank"><i class="fa fa-slideshare"></i></a>
* Kaggle 連結 <a href="https://www.kaggle.com/t/5808de7e75cf4e509d28d014a9f36f7d" target="_blank"><i class="fa fa-trophy"></i></a>
* 遲交表單 <a href="https://goo.gl/forms/DZ2Gz9Q5Fz4ucchP2" target="_blank"><i class="fa fa-file-text"></i></a>
* 小老師申請表單 <a href="https://goo.gl/forms/BcRm6cQtKmaARcIB3" target="_blank"><i class="fa fa-file-text"></i></a>
* Github <a href="https://github.com/ntumlta/2017fall-ml-hw2/tree/master" target="_blank"><i class="fa fa-github"></i></a>
* TA Hour Slide <a href="https://docs.google.com/presentation/d/1nOJkDRXDdORwkwibzX55w7jGOyB-rPjCOeRpwaZ82as/edit#slide=id.g1d460c2d77_0_60" target="_blank"><i class="fa fa-slideshare"></i></a>
<hr>
### Requirements
In this assignment, you are asked to implement the following two models.
#### <a href="./LogisticRegression.html" target="_blank">1. Logistic Regression</a>
Handcrafted Gradient Descent Optimizer to solve logistic regression.
#### <a href="./ProbabilisticGenerativeModel.html" target="_blank">2. Probabilstic Generative Model</a>
Implement probabilstic generative model to do binary classification.
<hr>
### Dataset : ADULT Dataset
#### Raw Data
* [train.csv](./raw_data/train.csv)
* [test.csv](./raw_data/test.csv)
* <a href="https://archive.ics.uci.edu/ml/datasets/Adult" target="_blank">Ref</a>
#### Feature
* [X_train](./feature/X_train)
+ 106 dims
+ one-hot encoding
* [Y_train](./feature/Y_train)
+ label = 0 表示小於等於50K, label = 1 表示大於50K
* [X_test](./feature/X_test)
#### Ans
* [Answer]
<hr>
### Rules
* Basic : No extra dataset.
* hw2_logistic.sh, hw2_generative.sh : Only toolkits below are allowed to use.
+ Python 3.5+
+ Numpy
+ Pandas
+ Python Standard Lib
* hw2_best.sh : 除了上述套件，可以使用以下套件，若有其他想用的請再來信詢問TA是否可以使用。
+ Tensorflow 1.3
+ Keras 2.0.8
+ Pytorch 0.2.0
+ Scikit-learn 0.19.0
+ XGBoost 0.6
+ h5py 2.7.0
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<center><i class="fa fa-envelope"></i> Contact information: <a href="mailto:"> [email protected] </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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