README.md

第二周：分类

学习内容

视频课程

全部视频已传至内部 FTP 服务器，下面提供的链接是 Bilibili 上的观看地址。

• Machine Learning (2017,Spring), 李宏毅，台湾大学
  • 逻辑回归（Logistic Regression）
• Machine Learning Techniques (機器學習技法)，林轩田，台湾大学
  • 01_Decision_Tree_Hypothesis
  • 02_Decision_Tree_Algorithm
  • 03_Decision_Tree_Heuristics_in_CRT
  • 04_Decision_Tree_in_Action

文字资料

• 图像分类（CS231n Notes）
• A Complete Guide to K-Nearest-Neighbors with Applications in Python and R

作业

In this assignment you will practice putting together a simple image classification pipeline, based on the k-Nearest Neighbor, logistic regression and decision tree classifier. The goals of this assignment are as follows:

• understand the basic Image Classification pipeline and the data-driven approach (train/predict stages)
• understand the train/val/test splits and the use of validation data for hyperparameter tuning.
• develop proficiency in writing efficient vectorized code with NumPy
• implement and apply a k-Nearest Neighbor (kNN) classifier
• implement and apply a Multiclass Logistic Regression classifier
• understand the differences and tradeoffs between these classifiers
• get a basic understanding of performance improvements from using higher-level representations than raw pixels (e.g. color histograms, Histogram of Gradient (HOG) features)

Ref: CS231n assignment 1