課程資訊
課程名稱
 數值分析導論
Introduction to Numerical Analysis 
開課學期
 108-2 
授課對象
 理學院  物理學系  
授課教師
 陳凱風 
課號
 Phys4009 
課程識別碼
 202 48160 
班次
  
學分
 3.0 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期一7,8,9(14:20~17:20) 
上課地點
 新物112 
備註
 總人數上限：50人 
Ceiba 課程網頁
 http://ceiba.ntu.edu.tw/1082Phys4009_ina 
課程簡介影片
 
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課程概述

We will introduce commonly used numerical methods in scientific computing in this lecture. The base computing language will be Python.

The slides and example codes will be available at
http://hep1.phys.ntu.edu.tw/~kfjack/lecture/numerical/2020/

The assignments should be handed to
http://hep12.phys.ntu.edu.tw/

We have already switched the whole class online, with Cisco Webex:
Link to the room:
https://ntucc.webex.com/ntucc/j.php?MTID=m77b2708b09e088b52d16e7216e0fb9e8
Password: 29979 

課程目標
 Here are the outline for the course:

Part I: Introduction to Python (slides only)
The basis / Control flow / Types and data? structure / Functions and modules / ?Input & Output / Classes and others

Part II: Numerical analysis basis
Error analysis / Numerical differential and integration / Random numbers / Linear algebra / Root finding and minimum finding / Differential equations / Visualization

Part III: Advanced topics
Machine learning / Data modeling and fitting / Statistical analysis 
課程要求
 待補 
預期每週課後學習時數
  
Office Hours
  
指定閱讀
 待補 
參考書目
 Python.org tutorial:? https://docs.python.org/3.6/tutorial/index.html
Think python (?slides are based on this book):? http://www.greenteapress.com/thinkpython/html/index.html
A byte of python: ? http://swaroopch.com/notes/python/
SciPy official web document:? http://docs.scipy.org/doc/
NumPy Beginner’s Guide:? http://it-ebooks.info/book/2847/
SciPy and NumPy book:? http://it-ebooks.info/book/1280/

An Introduction to Computational Physics, Tao Pang, 2nd Edition (2006, 2012)
Introduction to Computation and Programming Using Python, John V. Guttag (2016)
Computational Physics: Problem Solving with Python, Rubin H. Landau et al. 3rd Edition (2015)
Numerical Recipes: The Art of Scientific Computing, William H. Press, 3rd Edition (2007): http://www.nr.com/ 
評量方式
(僅供參考)
    
課程進度
週次
日期
單元主題
第3週
 3/02  Lecture 0-1: All you need to know about this course +
Lecture 2-1: The Art of Numerical Analysis 
第4週
 3/09  Lecture 2-2: Numerical Differential & Integration 
第5週
 3/16  Lecture 2-3: NumPy array & linear algebra (I) 
第6週
 3/23  Lecture 2-4: NumPy array & linear algebra (II) 
第7週
 3/30  Lecture 2-5: Root finding & minimization 
第8週
 4/06  Lecture 2-6: Solving ordinary differential equations 
第9週
 4/13  Lecture 2-7: Random numbers 
第10週
 4/20  Lecture 3-1: Brief on machine learning 
第11週
 4/27  Lecture 3-2: Incorporating Nonlinear Models 
第12週
 5/04  Lecture 3-3: Tricks for Improving Neural Network 
第13週
 5/11  Lecture 3-4: Deep Structured Learning 
第14週
 5/18  Lecture 3-5: Modeling of Data – Probability & Probability Distributions 
第15週
 5/25  Lecture 3-6: Modeling of Data – Parameter Estimation 
第16週
 6/01  Tournament + Final presentation (I) 
第17週
 6/08  Final presentation (II)