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10801 工程
test
教授
10801 工程
數位邏輯設計
林永隆 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
      Description of the course
This course is about the principles of digital logic and its implementation in circuits and systems. Modern electronics/computer engineers should be able to effectively utilize millions or even billions of logic gates available from advanced semiconductor manufacturing process. Logic design is fundamental to such applications as computer, audio/video/graphics, wired/wireless communication. Some technology from this course will be useful in other courses. For example, finite state machine is essential in compiler design.
   ♠ We will cover the following topics

1. Introduction to computer circuits
2. Two level Combinational Logic
3. Multilevel Combinational Logic
4. Programmable Logic
5. Arithmetic Logic Circuits
6. Sequential Logic Design
7. Sequential Logic Case Studies
8. Finite State Machine Design
9. Finite State Machine Optimization
10. Computer Organization

 

 

【課程教材】
      Course Material

  ♠  Logic and Computer Design Fundamentals, 4th ed Mano & Kime Prentice Hall

 

 【教學方式】
       Teaching Method

   Lecturing
Home work

Machine problem
(Using Verilog Hardware Description Language and its Simulator)
Quiz, Two Midterms and Final Exam

 

10801 工程
電路與電子學
林永隆 教授

This course is intended for 2nd-year undergraduates in the computer science and other engineering department. In this course, we focus on two main topics, electronics and circuits. 

 

課程說明 
      Description of the course

This course is intended for 2nd-year undergraduates in the computer science and other engineering department. In this course, we focus on two main topics, electronics and circuits. To give students better understanding of the models and circuits of the electronics devices, we will introduce the basic concepts of electric circuits first. It starts from the definition of current, voltage, power and energy. Then it covers the basic circuit components including resister, capacitor, and inductor. We also discuss the basic circuit analysis, such as nodal analysis of resistive circuits, transients in electrical circuits and sinusoidal steady-state circuit behavior.

For electronics section, we will present the diode and MOS field-effect transistor. We will use simple models to describe these electronic devices and their I-V characteristics. Since the majority of electronic circuits today are designed as integrated circuits (ICs),we will discuss how to analyze and design the basic elements of integrated circuits with the emphasis on digital logic designs.



課程教材
     Course Material 

   ♠ 

"Electrical Engineering - Principles and Applications",by Allan R. Hambley,
5th Edition.

 

教學方式  
       Teaching Method
   ♠ Lecture and discussion.


 教學進度 
      Schedule

   Chapter  1  Introduction
Chapter 2  Resistive Circuits
Chapter 3  Inductance and Capacitance
Chapter 4  Transients
Chapter 5  Steady-State Sinusoidal Analysis
Chapter 6  Frequency Plots, Bode Plots, and Resonance
Chapter 10  Diodes
Chapter 12  Field-Effect Transistors
Chapter 11  Amplifiers : Specifications and External Characteristics
     
     


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,(2)二階線性微分方程式,(3)高階線性微分方程式,(4)聯立微分方程式,(5)微分方程式級數解,(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

1. Dennis G. Zill, Advanced Engineering Mathematics, 6th edition, Jones & Bartlett Learning.
2. C. Henry Edwards and David E. Penney, Differential Equations and Boundary Value Problems: Computing and Modeling, 5th Edition, 2016, Pearson.
3. 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. Laplace transform
4. Series solutions for differential equations
5. Systems of differential equations
6. 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