課程資訊
課程名稱
社會科學基礎數學
Basic Mathematics for Social Science 
開課學期
110-1 
授課對象
社會科學院  政治學研究所  
授課教師
李宣緯 
課號
PS5710 
課程識別碼
322 U2460 
班次
 
學分
2.0 
全/半年
半年 
必/選修
選修 
上課時間
星期四8,9(15:30~17:20) 
上課地點
 
備註
上課教室社科305。
限學士班三年級以上
總人數上限:25人
外系人數限制:10人 
Ceiba 課程網頁
http://ceiba.ntu.edu.tw/1101PS5710_ 
課程簡介影片
 
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課程概述

This course is designed to provide students' knowledge of elementary mathematics and to expose them to some of the mathematical concepts and techniques that are required to study mathematical models in political and sociological sciences. Emphasis will be placed on the understanding of important concepts and developing analytical skills rather than just computational skills, the use of algorithms and the manipulation of formulae. We will focus on three integral mathematical tools used in social science: calculus, linear algebra, and optimization. This curriculum is been developed to complement and strengthen the quantitative methods course offerings for social science students at both the undergraduate and graduate levels.

Week 1: Preliminaries
Week 2: Functions, Relations, and Utility
Week 3: Limits and Continuity, Sequences and Series, and More on Sets
Week 4: Introduction to Calculus and the Derivative
Week 5: The Rules of Differentiation
Week 6: The Integral
Week 7: Extrema in One Dimension
Week 8: Midterm
Week 9: Vectors and Matrices
Week 10: Vector Spaces and Systems of Equations
Week 11: Eigenvalues and Markov Chains
Week 12: Multivariate Calculus
Week 13: Multivariate Optimization
Week 14: Comparative Statics and Implicit Differentiation
Week 15: Course Review
Week 16: Final Exam 

課程目標
Overall, this course should enhance the learning of students in their quantitative courses by providing them with a recent exposure to the baseline level of mathematical knowledge required in quantitative courses in the social sciences, particularly in political and sociological sciences. By ensuring students taking subsequent quantitative classes have a core set of skills the later classes can focus at a more sophisticated conceptual level. 
課程要求
成績評量方式與標準
(請說明各項評量項目內容設計、比例及標準)Grading
Quizzes 10%
Assignments 30%
Midterm exam 30%
Final exam 30%

Grades in the C range represent performance that is below expectations;
Grades in the B range represent performance that meets expectations;
Grades in the A range represent work that is excellent.

本課程對學生課後學習之要求
Requirements for students after the class: 1. An important component of this course is active engagement with the material in classes. Regular attendance is essential and expected.
2. There will be 2 problem sets during the semester, with 5 questions apiece, drawn mostly from the textbook. You are encouraged to discuss with your classmates about the problems, but you must write and turn in your own answers. To be blunt, rote copying of an answer from your classmates or other sources is a waste of your time and the grader's time.
3. Quizzes and exams are closed book, closed notes. Students are expected to study after classes.
4. No makeup exams will be given.
5. No foods in class. 
預期每週課後學習時數
 
Office Hours
 
參考書目
一、 指定閱讀(請詳述每週指定閱讀) Required readings

Moore, Will H., and David A. Siegel. A mathematics course for political and social research. Princeton University Press, 2013. (Moore and Siegel)

Week 1: Preliminaries
Chapter 1, Moore and Siegel
Week 2: Functions, Relations, and Utility
Chapter 2 and 3, Moore and Siegel
Week 3: Limits and Continuity, Sequences and Series, and More on Sets
Chapter 4, Moore and Siegel
Week 4: Introduction to Calculus and the Derivative
Chapter 5, Moore and Siegel
Week 5: The Rules of Differentiation
Chapter 6, Moore and Siegel
Week 6: The Integral
Chapter 7, Moore and Siegel
Week 7: Extrema in One Dimension
Chapter 8, Moore and Siegel
Week 8: Midterm
Week 9: Vectors and Matrices
Chapter 12, Moore and Siegel
Week 10: Vector Spaces and Systems of Equations
Chapter 13, Moore and Siegel
Week 11: Eigenvalues and Markov Chains
Chapter 14, Moore and Siegel
Week 12: Multivariate Calculus
Chapter 15, Moore and Siegel
Week 13: Multivariate Optimization
Chapter 16, Moore and Siegel
Week 14: Comparative Statics and Implicit Differentiation
Chapter 17, Moore and Siegel
Week 15: Course Review
Week 16: Final Exam

二、 延伸閱讀(請詳述每週延伸閱讀) Extension readings

1. Stewart, James. Calculus: Concepts and contexts. Cengage Learning, 2009. (Stewart)

2. Boyd, Stephen, and Lieven Vandenberghe. Introduction to applied linear algebra: vectors, matrices, and least squares. Cambridge university press, 2018. (Boyd and Vandenberghe)

Week 1: Preliminaries
Chapter 1, Boyd and Vandenberghe
Week 2: Functions, Relations, and Utility
Chapter 2, 3, and 4, Boyd and Vandenberghe
Week 3: Limits and Continuity, Sequences and Series, and More on Sets
Chapter 1, Stewart
Week 4: Introduction to Calculus and the Derivative
Chapter 2.1-2.5, Stewart
Week 5: The Rules of Differentiation
Chapter 2.6-2.9, Stewart
Week 6: The Integral
Chapter 4, Stewart
Week 7: Extrema in One Dimension
Chapter 3, Stewart
Week 8: Midterm
Week 9: Vectors and Matrices
Chapter 6 and 7, Boyd and Vandenberghe
Week 10: Vector Spaces and Systems of Equations
Chapter 8, Boyd and Vandenberghe
Week 11: Eigenvalues and Markov Chains
Chapter 9, 10, and 11, Boyd and Vandenberghe
Week 12: Multivariate Calculus
Chapter 12, Stewart
Week 13: Multivariate Optimization
Chapter 13 and 14, Stewart
Week 14: Comparative Statics and Implicit Differentiation
Chapter 15, Stewart
Week 15: Course Review
Week 16: Final Exam 
指定閱讀
待補 
評量方式
(僅供參考)
   
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