Semester	Spring Semester, 2025
Department	MA Program of Statistics, First Year MA Program of Statistics, Second Year
Course Name	Statistics and Machine Learning
Instructor	CHANG CHIH-HAO
Credit	3.0
Course Type	Elective
Prerequisite

Course Objective

Course Description

Course Schedule

Week	Topic	Content and Reading Assignment	Teaching Activities and Homework
1	Perceptron Learning Algorithm (PLA)	Perceptron Learning Algorithm (PLA)	PowerPoint and assign homework assignment for practicing PLA method in R or Python
2	Learning Types and Error Measures	Supervised or Unsupervised Learning and Weighted losses	PowerPoint and homework assignment for finding several learning algorithms that belong to which type of learning techniques
3	Regression Models	Linear Regression Models and Logistic Regression Models	PowerPoint and homework assignment for practicing linear methods in R or Python
4	Support Vector Machine (SVM)	Hard SVM and Dual SVM	PowerPoint and homework assignment for practicing SVM in R or Python
5	Support Vector Machine (SVM)	Soft SVM and Kernel SVM	PowerPoint and homework assignment for practicing kernel SVM in R or Python
6	Lasso	Lasso for high-dimensional regression Models	PowerPoint and homework assignment for practicing Lasso in R or Python
7	Kernel Logistic Regression (KLR) and Support Vector Regression (SVR)	KLR and SVR	PowerPoint and homework assignment for practicing KLR and SVR in R or Python
8	Midterm Exam	Midterm Exam	Midterm Exam
9	Blending, Bagging and Boosting	Blending, Bagging and Adaptive Boosting method	PowerPoint and homework assignment for practicing AdaBoost in R or Python
10	Random Forest	Decision Tree and Random Forest	PowerPoint and homework assignment for practicing Random Forest method in R or Python
11	Random Forest	Gradient Boosted Decision Tree (GBDT)	PowerPoint and homework assignment for practicing GBDT in R or Python
12	Neural Network	Neural Network (NN)	PowerPoint and homework assignment for practicing NN in R or Python
13	Radial Basis Function (RBF) Network	RBF Network and K-means algorithm	PowerPoint and homework assignment for practicing RBF Network and K-means in R or Python
14	Deep Learning	Brief Introducing Deep Learning	PowerPoint and homework assignment for practicing Deep Learning in R or Python
15	Text Mining	Brief Introducing Text Mining	PowerPoint and homework assignment for practicing Text Mining in R or Python
16	Final Exam	Final Exam	Final Exam

Teaching Methods

Teaching Assistant

Not Applicable.

Requirement/Grading

1. Midterm Exam (40%)：Physical examination and paper-based assessments will cover the content from the first to the seventh week of the course.

2. Final Exam (40%)：Physical examination and paper-based assessments will cover the content from the ninth to the fifteenth week of the course.

3. Regular homework assignments (20%).

Textbook & Reference

An Introduction to Statistical Learning with Applications in R. Second Edition. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

Urls about Course

Not Applicable

Attachment

Perceptron learning algorithm.pdf
Key Features in Learning.pdf
Ch3_Regression_Models.pdf
Support_Vector_Machine.pdf
Regularization.pdf
Tree_Based_Methods.pdf