Machine Learning 2020 Spring, Hung-yi Lee

Syllabus

• Requirement
• Homework 2
• Homework 3
• Homework 4
• PyTorch Introduction

Requirement

• Python 3.7.7 and PyTorch:

  torch==1.7.1+cu110
  torchaudio==0.7.2
  torchvision==0.8.2+cu110
  # On Colab
  torch==1.10.0+cu111
  torchaudio==0.10.0+cu111
  torchvision==0.11.1+cu111
  

• More detail in requirement.txt

Homework 2

• Report HW2 Classification

  Task - Income high or low

  Problem: Binary classification, to predict the income of an indivisual exceeds 50,000 or not.

  Method: logistic regression (linear classifier), linear dicriminant (generative model)

  Dataset: This dataset is obtained by removing unnecessary attributes and balancing the ratio between positively and negatively labeled data in the Census-Income (KDD) Data Set, which can be found in UCI Machine Learning Repository. 該數據集包含從美國人口普查局進行的1994年和1995年的當前人口調查中提取的加權普查數據。數據包含41個與人口和就業相關的變量。

  Reference:

  1. Detail description of HW2 Classification
  2. Colab sample code

Homework 3

• Report HW3 CNN

  Task - Food Classification

  此資料集Food-11為網路上蒐集到的食物照片，共有11類:

  • Training set: 9866張
  • Validation set: 3430張
  • Testing set: 3347張

  category	類別
  Bread	麵包
  Dairy product	如起司、牛奶、奶油
  Dessert	甜食
  Egg	蛋
  Fried food	炸物
  Meat	肉類
  Noodles/Pasta	麵食
  Rice	米飯
  Seafood	海鮮
  Soup	湯
  Vegetable/Fruit	蔬菜水果

  Reference:

  1. Detail description of HW3 CNN
  2. Kaggle competition
  3. Colab sample code

Homework 4

• Report HW4 RNN

  Task - Text Sentiment Classification

  資料為Twitter上收集的推文，每則推文都會被標註為正面(1)或負面(0)，如: thanks! i love the color selectors. btw, that's a great way to search and list. (LABEL=1)、 I feel icky, i need a hug. (LABEL=0)

  除了labeled data以外，我們還額外提供了120萬筆左右的 unlabeled data:

  • labeled training data ：20萬
  • unlabeled training data：120萬
  • testing data：20萬（10 萬 public，10 萬 private）

  Preprocessing the sentences:

  • 首先建立字典，字典元素為每一個字與其所對應到的index value。
    • e.g. I have a pen. → [1, 2, 3, 4]; I have an apple. → [1, 2, 5, 6]
      where {'I': 1, 'have': 2, 'a': 3, 'pen': 4, 'an': 5, 'apple': 6}
  1. 利用word embedding來代表每一個單字。亦即，用一個向量來表示字(或詞)的意思。
  2. 利用bag of words (BOW)方式得到代表該句子的vector

  Semi-supervised Learning:

  • 使用unlabeled data協助模型訓練，如Self-Training

  Reference:

  1. Detail description of HW4 RNN
  2. Colab sample code

PyTorch Introduction

• Start PyTorch

  - start_Pytorch.py
  - start_dataloader.py
  - start_buildnn.py
  - start_autograd.py
  - start_optimizer.py

  Reference:
  1. PyTorch documentation(ver. 1.10 stable release)
  2. Cheat sheet organized by Chen