課程大綱

課程資訊

課程名稱	數值分析導論 Introduction to Numerical Analysis
開課學期	107-2
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/1072Phys4009_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/2019/ The assignments should be handed to http://hep12.phys.ntu.edu.tw/
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	每週四 15:00~17:00 備註： TA office hour: Wed 14:30 - 16:30
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1週	02/18	Introduction: All you need to know about this course
第2週	02/25	Lecture 2-1:  The Art of Numerical Analysis
第3週	03/04	Lecture 2-2:  Numerical Differential & Integration
第4週	03/11	Lecture 2-3:  NumPy array & linear algebra (I)
第5週	03/18	Lecture 2-4:  NumPy array & linear algebra (II)
第6週	03/25	Lecture 2-5:  Root finding & minimization
第7週	04/01	Lecture 2-6:  Solving ordinary differential equations
第8週	04/08	Lecture 2-7:  Random numbers
第9週	04/15	Announcement: final project, tournament, and mid-term quizzes
第10週	04/22	Lecture 3-1:  Brief on machine learning
第11週	04/29	Lecture 3-2:  Incorporating Nonlinear Models
第12週	05/06	Lecture 3-3:  Tricks for Improving Neural Network
第13週	05/13	Lecture 3-4:  Deep Structured Learning
第14週	05/20	Break
第15週	05/27	Lecture 3-5:  Modeling of Data: Probability & Probability Distributions
第16週	06/03	Lecture 3-6:  Modeling of Data: Parameter Estimation
第17週	06/10	Tournament + Final project presentation