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Scientific Computing科學計算
Roger Jang (張智星 )[email protected]
http://mirlab.org/jangCSIE Dept, National Taiwan
University
Scientific Computing: Introduction
Background “Linear Algebra” and
“Numerical Methods” tends to be too dry… Numerous theorems
Rank, null space… Linear transformation Eigenvalues Gauss elimination QR decomposition …
Where is the application?
“Scientific Computing” come to the rescue! A course to bridge the
gap between LA/NM and applications
With emphases on Problem solving Hands-on coding Data/approach
visualization Real-world applications
Scientific Computing: Introduction
Objectives (1/2) Programming
Matrix computation Programming
paradigms Animation &
Visualization Basics of audio and
images …
Methodologies Least-squares estimate Approximation Interpolation PDF modeling Data clustering Pattern recognition Dynamic programming Numerical optimization …
Scientific Computing: Introduction
Objectives (2/2) Applications
Personal financial computing
Loan and mortgage Insurance
Least-square estimate Data fitting
Data clustering Image data compression Object identification
Classification Texts, audio, images…
Principal component analysis
Dimensionality reduction Data fitting
Page rank Google’s page rank Team ranking
Dynamic programming Object tracking
Fractals
Scientific Computing: Introduction
Prerequisites Prerequisites for STEM (science,
technology, engineering, and mathematics) students Calculus: must Linear algebra: must Probability: better to have taken it already
For non-STEM students Please talk to me first before taking the
course
Scientific Computing: Introduction
Text and Reference Books Textbooks
MATLAB程式設計【入門篇】 by Roger Jang
Online tutorial of Data Clustering and Pattern Recognition by Roger Jang
Note that there is no single book that covers all aspects of scientific computing.
Reference books Experiments with MA
TLAB by Cleve Moler
Numerical computing with MATLAB by Cleve Moler
MATLAB程式設計【進階篇】 by Roger Jang
Scientific Computing: Introduction
Corpus Collection We need to collect corpus for
assignments Face recognition Your face photos Query by humming Your singing Speaker recognition Your voice Age estimation from face Your face
photos at different ages …
Scientific Computing: Introduction
Instructor and TAs Instructor
Roger Jang (張智星） Email:
[email protected] Phone
0953-154-045 Skype: roger_jang
Office hour: Call/email me any time to arrange an appointment (leave a message if necessary)
TAs NTU
NTHU 卓真弘
Scientific Computing: Introduction
Grading Policy (1/2) Course
participation: 10% Each in-class
question asked: +2% (10% top)
Interactions with TAs and fellow students (over FB, CEIBA, Email, BBS, etc.)
Roll call: -2% for each absence (no lower bound)
Assignments: ~30% Demo required for
programming assignments
Flipped learning: ~20% Review exams Questions and answers
Midterm & final exams: ~40% Hand-written Programming
Scientific Computing: Introduction
Grading Policy (2/2) Note that…
The instructor reserves the right to fine-tune those percentages
All course material, assignments, exams, grading policy, etc. are more or less the same for both NTU and NTHU.
The instructor will be generous in grading, given that you have fulfilled all the requirements!
Scientific Computing: Introduction
Grading Policy (3/3) 在 FB社團回答同學問題，每回答三次，可以抵課堂發問一次，但還必須滿足下列條件： 必須是與課程內容相關之技術性問題 回答必須正確且完整 同學們必須自行回報給助教，由助教審核認定
Scientific Computing: Introduction
Demo Time NTU
Time: Tuesday 7-10pm
Place: CSIE Dept, 219 Computer room
NTHU Time: Monday 7-10
pm Place: CS Dept, 326
Computer room
Scientific Computing: Introduction
Similar Courses Similar courses in the US
Introduction to Scientific Computing and Problem Solving (CS Dept, Brown Univ)
Introduction to Scientific Computing (School of Computing, Univ. of Utah)
Scientific Computing (Math Dept, New York Univ)
Scientific Computing in MATLAB (Math Dept, U. of Colorado)
Scientific Computing: Introduction
Important Websites Websites for this course
Course homepage Facebook group CEIBA (NTU only) MATLAB resources
MATLAB程式設計入門篇 MATLAB程式設計進階篇 Toolboxes written by Roger Jang
Scientific Computing: Introduction
Other Reminders Be aware that
All latest news on FB (no further email notification)
A+ for recommendation letter Final grades are based on both
Absolute scores Ranking
Homework score is exponentially decayed (0.8)^n
