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10801 人文社會
經濟思辨一
朱敬一 教授

本課程擬從一個個體經濟概念開始,引領進入不同的應用領域。除了經濟,這門課打算 串接法律學、演化生物學、國際關係等許多學門,希望能幫助非財經本科學生理解經濟 觀念如何應用在多面向問題的分析上。 

 

 

【課程目標
       Course Objectives
   本課程擬從一個個體經濟概念開始,引領進入不同的應用領域。除了經濟,這門課打算 串接法律學、演化生物學、國際關係等許多學門,希望能幫助非財經本科學生理解經濟 觀念如何應用在多面向問題的分析上。  

 

 
 【課程資訊】
       Course Information
上課時間:

每周三第5堂至第7堂 (13:20-16:20)

上課地點: 台積館 - 孫運璿演講廰  (111教室)


 
【課程說明 】
       Course Description   
     這門課不想教大家「制式」的經濟分析,而要從一個個體經濟概念開始,引領進入不同 的應用領域。除了經濟,這門課打算串接法律學、演化生物學、國際關係等許多學門, 希望能幫助非財經本科學生理解以下問題:     
 


1.為什麼要唸經濟思辨? 
       1a. 什麼是「通識」經濟學?
       1b. 為什麼只懂數學的咖能在財經科系招搖撞騙? 


2.需求、供給、完全競爭均衡  延伸閱讀 
       2a. 生物界的完全競爭 -- 為什麼海豚聰明又長壽?
       2b. 語言演變的競爭規 (原,哪裡來那麼多的「不規則動詞」?  


3.從傳統經濟到創新經濟
       3a. 究竟什麼是知識經濟?
       3b. 知識研發的特殊角色
       3c. 為什麼川普要對「七年後的產品」課稅? 


4.網路經濟時代的競爭
       4a. 網路經濟下的競爭與定價
       4b. 電子商務的國際競爭


5.生產要素的報酬與分配
       5a. 全球化下的「異形」勞資關係
       5b. 勞動基準法與罷工


6.所得與財富分配的不均
       6a. 該不該課徵遺產贈與稅?
       6b. 台灣所得分配不均的幾個觀察
       6c. 股利分離課稅,有道理嗎?


7.國際貿易的理論與挑戰
       7a. 川普何時決定貿易戰「收兵」?
       7b. 自由貿易之外,政府究竟該不該有「產業政策」?



8.市場失靈與永續發展
       8a. 在市場失靈與政府失靈之間
       8b. 從永續發展理念,談「九二共識」的衍生應對 



9.資訊經濟學
       9a.孔雀開屏傳遞了什麼訊息?
       9b.家族企業裡的訊息不對稱



10.經濟發展過程中家族角色的蛻變
       10a. 超級富豪家族成員之間,彼此親近嗎?
       10b. 股票投資,為什麼女性比男性厲害?
       10c. 土地投資,為什麼台灣男性比女性厲害?


11.經濟轉型與經濟困境
       11a. 中國與蘇聯不同的轉型策略
       11b. 從韜光養晦到一帶一路


12.不完全競爭市場
       12a. 陸域風電與離岸風電背後的不同故事   
       12b. 為什麼 Amazon 拿不下紅白葡萄酒的零售市場?   


13.國際金融
       13a. 台灣的外匯存底
       13b. 歐債危機是怎麼回事

   
   
10801 工程
工程數學
蔡仁松 教授

工程數學為一切工程學科的基礎,提供解決工程問題的基本工具。

 


【課程說明】

      Description of the course
     工程數學為一切工程學科的基礎,提供解決工程問題的基本工具。預修科目為微積分。課程內容包括:

  (1) 一階微分方程式 (4) 聯立微分方程式
  (2) 二階線性微分方程式 (5) 微分方程式級數解
  (3) 高階線性微分方程式 (6) 拉普拉斯轉換

 


【課程教材】

        Course Material

   Erwin Kreyszig, Advanced Engineering Mathematics, 10th edition, 2011, Wiley.


 

【參考教材】
         References 

 ♠ 

Dennis G. Zill, Advanced Engineering Mathematics, 6th edition, Jones & Bartlett Learning.

  

C. Henry Edwards and David E. Penney, Differential Equations and Boundary Value Problems: Computing and Modeling, 5th Edition, 2016, Pearson.

 ♠

Peter V. O'Neil, Advanced Engineering Mathematics, 8th edition, 2017, Thomson Brooks/Cole.


 

【教學方式】
       Teaching Method

 Lectures and labs.

 

 

【教學進度】
       Schedule

   1. First order differential equations
   2. Second order differential equations
   3. Systems of differential equations
   4. Numerics for order differential equations
  5. Series solutions for differential equations
  6. Laplace transform
  7. Fourier series

 

10801 工程
資料結構
蔡仁松 教授

This course introduces the basic concept of data representation and manipulation. 


 

課程說明
     Description of the course

This course introduces the basic concept of data representation and manipulation. We will teach how to solve problems efficiently and effectively by using proper and specific data structures, and organizing series of operations called algorithms to manipulate data to solve the problems. For instance, you will be ble to understand how to use link list and hash function to create block chains.


前導課程
     prerequisite Course

  ♠  C/C++ Programming Language



課程教材
      Course Material 

 ♠  Fundamentals of Data Structures in C++, E. Horowitz, S. Sahni, and D. Mehta, 2nd ed., 2006.

 


參考教材
      References 

   Introduction to Algorithms, 3rd ed., by Cormen et al. C++ reference 


教學方式
      Teaching Method 

  Online Lectures + In class discussions


 

 教學進度
       Schedule

1. Basic Concepts
2. Arrays
3. Stacks and queues
4. linked lists
5. Trees
6. Graphs
7. Sorting
8. Hashing
9. Selected related topics

 

10702 工程
計算機程式設計一(資工版)
陳煥宗 教授

This course is aimed to help the students learn how to program in C. There will be several labs, two midterm exams, one final exam, and the final project, with the following percentages.......

  

Text Books 
 
*
S. Prata, C PRIMER PLUS 
*
Lecture notes 
  https://github.com/htchen/i2p-nthu/tree/master/程式設計一
*
清大開放課程影片(17週)
  http://ocw.nthu.edu.tw/ocw/index.php?page=course&cid=134
 
 
 
  
 Reference= ilms  
* Essential C
  http://cslibrary.stanford.edu/101/EssentialC.pdf
*
The C Book
  http://publications.gbdirect.co.uk/c_book/the_c_book.pdf 
*

MIT: A Crash Course in C

  http://www.mattababy.org/~belmonte/Teaching/CCC/handouts.pdf
* MIT: A Crash Course in C
  Reference Manual
  http://www.gnu.org/software/libc/manual/html_mono/libc.html
 
 
Syllabus   
  
 

Week

Topics

Labs and Exams

1

 

2/19,2/21

CH. 1 Getting Ready

CH. 2 Introducing C

 

Lab #0 2/21 Thu.

2

 

2/26,2/28

CH. 3 Data and C

CH. 4 Formatted Input/Output

2/28 放假

3

 

3/5, 3/7

CH. 4 Formatted Input/Output

Lab #1 3/7 Thu.

4

 

3/12, 3/14

Binary Representations

CH. 15 Bit Manipulation

CH. 5 Operators, Expressions, and Statements

 

5

 

3/19,3/21

CH. 6 Control Statements: Looping

Lab #2 3/21 Thu.

6

 

3/26,3/28

CH. 6 Control Statements: Looping CH. 7 Control Statements: Branching

Written Exam  3/28 Thu. @ Delta 109

7

 

4/2, 4/4

CH. 8 Character I/O and Redirection

4/4 放假

8

 

4/9, 4/11

CH. 9 Functions

Recursion

Lab #3 4/11 Thu.

9

 

4/16, 4/18

CH. 9 Functions

Recursion

 

10

 

4/23,4/25

CH. 10 Arrays and Pointers

Arrays

 

Midterm Exam I  4/25 Thu.

11

 

4/30, 5/2

CH. 10 Arrays and Pointers

Pointers

12

 

5/7, 5/9

CH. 10 Arrays and Pointers

Pointers

Lab #4 5/9 Thu.

13

 

5/14,5/16

Midterm Exercise (5/14)

Midterm Exam II 5/16 Thu.

14

 

5/21, 5/23

CH. 10 Arrays and Pointers

Pointers

CH. 11 String Functions

 

CH. 12 Memory Management

CH. 13 File Input/Output

CH. 14 Structures


Term Project Hackathon 5/25 Sat.

15

 

5/28,5/30

CH. 12 Memory Management

CH. 14 Structures

Lab #5 5/30 Thu.

16

 

6/4, 6/6

CH. 15 Bit Manipulation

CH. 14 Structures

CH. 17 Advanced Data Representations

Linked Lists

 

17

 

6/11,6/13

CH. 17 Advanced Data Representations

Lab #6 6/13 Thu.

18

 

6/18,6/20

No class

 

 

Final Exam 6/20 Thu.


Final Project Demo 6/25 Next Tue.

 
10702 工程
深度學習
吳尚鴻 教授
This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part......
 
  

【Description】
This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction of classical machine learning and review some key concepts required to understand deep learning.In the second part, we discuss how deep learning differs from classical machine learning and explain why it is effective in dealing with complex problems such as the image and natural language processing. Various CNN and RNN models will becovered. In the third part, we introduce the deep reinforcement learning and its applications.This course also gives coding labs. We will use Python 3 as the main programming language throughout the course. Some popular machine learning libraries such as Scikit-learn and Tensorflow will be used and explained in detials.

 
 
【Syllabus】  
Lecture 01  
Introduction/Scientific Python 101
Lecture 02     
Linear Algebra/Data Exploration & PCA
Lecture 03
Probability & Information Theory/Decision Trees & Random Forest

Lecture 04

Numerical Optimization/Perceptron & Adaline/Regression
Lecture 05
Learning Theory & Regularization /Regularization
Lecture 06
Probabilistic Models/Logistic Regression & Metrics 
Lecture 07
Non-Parametric Methods & SVMs/SVMs & Scikit-Learn Pipelines
Lecture 08
Cross Validation & Ensembling/CV & Ensembling

Competition01  

Predicting Appropriate Response
Lecture 09
Large-Scale Machine Learning
Lecture 10
Neural Networks: Design/TensorFlow101 & Word2Vec
Lecture 11
Neural Networks: Optimization & Regularization
Lecture 12

Convolutional Neural Networks/Nuts and Bolts of Convolutional Neural Networks/Visualization and Style Transfer

Competition 02

Image Object Detection & Localization

Lecture 13

Recurrent Neural Networks/Seq2Seq Learning for Machine Translation
Competition 03 Image Caption
Lecture 14
Unsupervised Learning/Autoencoders/GANs
Competition 04 Reverse Image Caption 
Lecture 15
Semisupervised/Transfer Learning and the Future
Lecture 16
Reinforcement Learning/Q-learning
Lecture 17
Deep Reinforcement Learning/ DQN & Policy Network
Competition 05 You Draw I Draw
   
 

 

【Reference Books】 
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016, ISBN: 0387848576
Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, 2009, ISBN: 0387848576
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 0387310738
Sebastian Raschka, Python Machine Learning, Packt Publishing, 2015, ISBN: 1783555130
  
 
 
【Online Courses】  
CS231n: Convolutional Neural Networks for Visual Recognition, Stanford
 CS224d: Deep Learning for Natural Language Processing, Stanford
CS 294: Deep Reinforcement Learning, Berkeley
 MIT 6.S094: Deep Learning for Self-Driving Cars, MIT
   
10702 工程
Web Programming, Technologies, and Applications
吳尚鴻 教授

This course gives a comprehensive, self-contained, and up-to-date introduction to the web/app development. We focus on the development challenges in real-world situations and present guidelines, tools, and best practices. Students are asked to team up and build real, useful applications (websites and/or mobile apps) accessible to the public in the end.

 

【 Description 】
The classes are divided into three parts. First, we give a primer to web fundamentals such as HTTP, HTML, CSS, and Javascript. We cover different programming paradigms, including the OOP and functional programming. Handy tools such as Git are covered to get students familiar with the project-based and team-based development. In the second part, we introduce modern web development techniques such as responsive design, Bootstrap, ES6/7, React, and Redux. Last, we extend our horizon to the backend and mobile development landscapes by introducing the Node.js, PostgreSQL database system, Amazon Web Services (AWS), and React Native. We also give case studies on how to leverage Machine Learning algorithms to convert raw user data into the AI.
 ♠ https://nthu-datalab.github.io/webapp/index.html


 
 

【 Syllabus 】
 
Lecture 01 HTTP&HTML
Lecture 02 CSS
Lecture 03 Bootstrap and Responsive Design
Lecture 04 Javascript & DOM
Lecture 05 Modern Javascript
   


 

【 Reference Books 】

Alexander Osterwalder, Business Model Generation: A Handbook for 
  Visionaries, Game Changers, and Challengers, 2010
Eric Ries, The Lean Startup: How Today's Entrepreneurs Use Continuous 
  Innovation to Create Radically Successful Businesses, 2011
Peter Thiel, Blake Masters, Zero to One: Notes on Startups, or How to Build
  the Future, 2014
   
   

 

10701 人文社會
台灣的水文
陳鸞鳳 教授

尋找興趣,提早準備,贏在起跑點!!想追求更多課本以外的專業知識嗎? 清華大學開放式課程為你種植了一座學習資源森林,等你來探索!現在就走進開放式課程的森林,品嚐最甜美的知識果實!
 

 

【課程說明】
 

【   周   次  】  【      教      學     內     容   】 

第  1  講

 

 

 * 地球上的水與台灣水資源
 
  The Water on Earth and the Water Resources in
    Taiwan


第  2  講

 

 * 地下水
    Groundwater

 

第  3  講

 

 * 水資源的保育 
    Conservation of Water Resources

第  4  


 

 台灣的陂塘
     Ponds in Taiwan

第  5  

 

 水的議題 
    Issues of Water

 

10701 人文社會
台灣的地名
陳鸞鳳 教授

尋找興趣,提早準備,贏在起跑點!!想追求更多課本以外的專業知識嗎? 清華大學開放式課程為你種植了一座學習資源森林,等你來探索!現在就走進開放式課程的森林,品嚐最甜美的知識果實!

 

 【課程說明】

      Course Description 

周 次   【            教            學          內            容       】 


 
* 1-1 地名的特性、結構與分類
             
Characteristics, Structure, and Catalogs of Toponomy
 
* 1-2 地名的演變
                Evolution of Toponomy
 
* 1-3 跟農業有關的地名 
               Toponomy related to Agriculture

* 1-4 以動植物命名的地名
                The Toponomy Using Names of Animals and Plants

 
 


 
 
* 2-1 從地圖上認識台灣的地名
             Knowing Names of Places in Taiwan through Maps 
 
* 2-2 跟族群有關的地名 
               Names of Places Related to Ethnic Group 
 
* 2-3 從地圖上認識台灣的其他地名
         Knowing Other Names of Places in Taiwan through Maps
 
* 2-4 台灣堡圖中的舊地名(一) 
                Old Names of Places in Earlier Topographic Map in Taiwan(I)

* 2-5 台灣堡圖中的舊地名(二)
                Old Names of Places in Earlier Topographic Map in Taiwan(II)
 

 

10701 工程
平行程式
周志遠 教授

本課程將介紹平行計算的基礎觀念和電腦系統架構,並教授針對不同平行計算環境所設計的程式語言,包括多核心系統使用的 Pthread、OpenMP, 叢集計算使用的MPI, GPU使用的CUDA, 以及分散式系統使用的MapReduce計算框架。修課同學必須使用 這些平行計算的語言和工具完成5個程式作業,並且以程式的執行效能結果作為學習的評量標準。 

 

 【課程說明   Course Description
       
    本課程將介紹平行計算的基礎觀念和電腦系統架構,並教授針對不同平行計算環境所設計的程式語言,包括多核心系統使用的 PthreadOpenMP, 叢集計算使用的MPI, GPU使用的CUDA, 以及分散式系統使用的MapReduce計算框架。修課同學必須使用 這些平行計算的語言和工具完成5個程式作業,並且以程式的執行效能結果作為學習的評量標準。 

 
 

【指定用書   Textbooks 
      

1.  


Parallel Programming
– Techniques and applications Using Networked 
Workstations and Parallel Computers, Barry Wilkinson and Michael Allen, Prentice Hall, 1999. 

2.


Parallel Programming in C with MPI and OpenMP, Michael J. Quinn, McGraw- Hill, 2003. 

3. Intel Multi-Core Programming 

   

 

【參考書籍   References

1.  

Documentation
 (PVM, MPI, Cilk, Pthread, TreadMark, SAM) 

2. Designing and Building Parallel Programs, Ian Foster, Addison Wesley, 1995. 

 

 

【教學進度 Syllabus     

Part I  Introduction 
       - Introduction to Parallel Computers  
     - Introduction to Parallel Computing 
 
Part II  Parallel Programming
 
     - Message-Passing Programming (MPI)  
     - Shared Memory Programming (Pthread and OpenMP) 
 
Part III  Parallel Computing Techniques 
       - Embarrassingly Parallel Computations  
     - Partitioning and Divide-and-Conquer Strategies  
     - Pipelined Computations  
     - Synchronous Computations  
     - Load Balancing and Termination Detection 
 
 Part IV  GPU Programming 
      - Heterogeneous computing  
    - CUDA programming model  
    - GPU Architecture & Multi-GPU  
    - Advanced CUDA Programming & Optimization 
 
 Part V  Distributed Programming 
      - MapReduce  
    - Hadoop Programming 

 

 

 

10602 自然科學
高等微積分二
高淑蓉 教授
本課程以訓練嚴謹的邏輯推導、撰寫明確的證明、流利的口語表達為手段,追求如何思考、如何有效的學習為目標。
 
 
【課程簡述】
     Brief course description 
    本課程以訓練嚴謹的邏輯推導、撰寫明確的證明、流利的口語表達為手段,追求如何思考、如何有效的學習為目標。 
    This course is based on teaching you how to think logically, how to prove clearly and 
express yourself fluency.Also,how to think and how to learn effectively and the goals of this course.
  
 
 
【課程說明】
      Course Description 
    本課程以訓練嚴謹的邏輯推導、撰寫明確的證明、流利的口語表達為手段,追求如何思考、如何有效的學習為目標。本課程內容探討歐幾里德空間的多變數函數之解析理論。本學期將介紹以下題材: 
    This course is based on teaching you how to think logically, how to prove clearly and express yourself fluency.Also,how to think and how to learn effectively and the goals of this course.The content of this course includes  the analysis of the multivariable functions in Euclidean space. The following topics are introduced in this semester:
  
 1. Differentiable Mappings 
 2. The Inverse and Implicit Function Theorems and Related Topics 
 3. Integration 
 4. Fubini's Theorem and The Change of Variables Formula 
 5. Fourier Analysis 
 
 
 
【指定用書】
           Text Books  
   *   
  second edition.
 
 
 
【參考書籍】
           References  
D. Widder, Advanced Calculus. 
T. Apostal, Advanced Calculus. 
 
 

【教學方式/教學進度】
              Teaching Method/Syllabus
    黑板授課/循序漸進   
     Teaching with Blackboard / Step by step