Linear Algebra, Fall 2007.

A undergraduate course in Mathematics at the Department of Mathematics, National Tsing Hua University.

Instructor: Professor Wei-Cheng Wang,

Class hours: General III Building, room 201, TF 1:10-2:25 PM

TA: §õ«FªÚ (ext 33126, Tuesday recitation) and ¼B­^³Ç (ext 33184, Wednesday recitation)  

Grading: 40% quiz, 20% midterm1, 20% midterm2 and 20% final exam.

Textbook and References: Textbook: Steven J. Leon: Linear Algebra with applications, 7th edition.

Course description, lecture notes (draft) and homework assignments:

Part I : Matrices and Systems of Equations.

Part II : Determinants.

Part III : Vector Spaces.

Part IV : Linear Transformations.

Part V : Orthogonality and Inner Product Spaces.

Part VI : Eigenvalus and Eigenvectors.

Syllabus: Detail course information including teaching schedule, quiz and exam dates.

Matlab, Octave and Rlab Information:

The textbook is acompanied with matlab exercises. Due to time constraint, we are unlikely to cover matlab materials. Here are some information about learning matlab from scratch. In case of sufficient demand, I can give a tutorial lecture on matlab sometime during the semester.

Matlab is a conveneint tool for matrix computations. Matlab is also an excellent environment for experimentation in scientific computing. It is not the most efficient environment for large-scale simulations, although with some effort, Matlab can be used in conjunction with C and Fortran routines. Matlab is a product of The Mathworks. For a tutorial, see A Free Matlab Online Tutorial or Another Tutorial or Matlab Primer.
You can start from our class note on matlab before reading these materials. Octave and Rlab are free (source code open) softwares also good for matrix computations. Octave is designed to be compatible with matlab. Matlab users should have no problem using Octave. Rlab, on the other hand, is meant not to be a clone of matlab. For more information on Octave and Rlab, visit here.
Homework Assignments:

   Homework for week 01.