Skip to content

NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.

Notifications You must be signed in to change notification settings

howardyclo/NTHU-Machine-Learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

36 Commits
 
 
 
 
 
 
 
 

Repository files navigation

NTHU-Machine-Learning

NTHU EE6550 Machine Learning slides and my code solutions for spring semester 2017.

Notes

  • The assignments are mainly required to implement the ML/DL algorithms from scratch without using high-level library like scikit-learn, all are coded with python/numpy.

Assignments

  • PAC Learning Rectangle
  • Linear SVM: Linear Support Vector Machine for Binary Classification trained with Sequential Minimal Optimization.
  • Kernel SVM and Adaboost
    • An Extension of HW2 linear SVM.
    • Adaboost classifer with the shallow decision tree (depth 1) for binary classification.
  • Kernel SVR: Support Vector Regression, trained with Sequential Minimal Optimization.
  • Neural Network: A simple neural network trained with SGD for regression.
  • Final Project: A solution report of Kaggle competition - Quora Question Pairs. Using various deep neural nets and XGboost to identify duplicate questions.