Course Description/課程描述:
Machine Learning is a popular research area lately. The main goal is to investigate the high dimensional, large amount or complex data, and to develop the useful algorithms to discover information within data.
機器學習是一個熱門研究領域，主要探討如何在高維度、大量或複雜資料中，發展不同的演算法來發掘資料中所隱藏的有用資訊。
Course Objectives/課程目標:
1. Introduce different methods in Machine Learning.
2. Student is ability to analyze massive and complicate data.
3. Student is capable to do programming based on the different algorithms.
Course Content/課程內容:
Week 01：Introduction
Week 02：Statistical Learning
Week 03：Linear Regression
Week 04：Linear Regression
Week 05：Linear Regression
Week 06：Classification
Week 07：Classification
Week 08：Classification
Week 09：Resampling Methods
Week 10：Resampling Methods
Week 11：Linear Model Selection and Regularization
Week 12：Linear Model Selection and Regularization
Week 13：Tree-Based Methods
Week 14：Tree-Based Methods
Week 15：Support Vector Machines
Week 16：Support Vector Machines
Week 17：Deep Learning
Week 18：Deep Learning
Textbook/教科書:
An Introduction to Statistical Learning with Applications in R 2nd Edition, By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Grading Policy/評量方式:
1. Term Project Report or fincal exam 40%
2. Homework 60%
- 教師(teacher): 陳瑞彬