Course Information
Course title
 Introduction to Statistical Learning 
Semester
 109-2 
Designated for
 COLLEGE OF SOCIAL SCIENCES  GRADUATE INSTITUTE OF POLITICAL SCIENCE  
Instructor
 HUAN-KAI TSENG 
Curriculum Number
 PS5696 
Curriculum Identity Number
 322EU2320 
Class
  
Credits
 2.0 
Full/Half
Yr.
 Half 
Required/
Elective
 Elective 
Time
 Tuesday 8,9,10(15:30~18:20) 
Remarks
 Restriction: juniors and beyond
The upper limit of the number of students: 30.
The upper limit of the number of non-majors: 10. 
Course
Website
 https://www.dropbox.com/sh/qcq5ddmf46xu20l/AACXG9ul5dcHMzQE0tjiEUA1a?dl=0 
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
 Association has not been established
Course Syllabus
Please respect the intellectual property rights of others and do not copy any of the course information without permission
Course Description

 

Course Objective
  
Course Requirement
  
Student Workload (Expected weekly study hours before and/or after class)
  
Office Hours
 Appointment required. Note: By appointment 
Designated reading
  
References
  
Grading
  
  1. NTU recommends an upper limit of 20% for A+ grades. This is not a mandatory requirement. Instructors may adjust the percentage based on course requirements. Instructors teaching required courses are particularly encouraged to follow this guideline.
  2. NTU uses a letter grade system for assessment. The grade percentage ranges and the single-subject grade conversion table in the NATIONAL TAIWAN UNIVERSITY Regulations Governing Academic Grading are for reference only. Instructors may adjust the percentage ranges according to the grade definitions. For more information, see the Assessment for Learning Section.
 
Progress
Week
Date
Topic
Week 1
 2/23  Introduction 
Week 2
 3/2  Regression Methods I 
Week 3
 3/9  Regression Methods II 
Week 4
 3/16  Dimensionality and Preprocessing 
Week 5
 3/23  Nonlinear Regression I 
Week 6
 3/30  Nonlinear Regression II 
Week 7
 4/6  No Class 
Week 8
 4/13  Statistical Learning I 
Week 9
 4/20  Statistical Learning II 
Week 10
 4/27  Short Film and Term Project Q & A 
Week 11
 5/4  Statistical Learning III 
Week 12
 5/11  Dimensionality Reduction and Prediction Accuracy 
Week 13
 5/18  Feature Engineering 
Week 14
 5/25  Resampling Methods 
Week 15
 6/1  Text mining 
Week 16
 6/8  Drawing Inference from Text Data 
Week 17
 6/15  Course wrap-up (or Social Network Analysis (TBD)) 
Week 18
 6/22  Final (no lecture, no class)