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Course, academic year 2018/2019
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Linear Algebra I - NMAI057
Title in English: Lineární algebra I
Guaranteed by: Department of Applied Mathematics (32-KAM)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018 to 2018
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: doc. Mgr. Milan Hladík, Ph.D.
doc. Mgr. Petr Kolman, Ph.D.
Class: Informatika Bc.
Classification: Mathematics > Algebra
Annotation -
Last update: Andrew Goodall (11.10.2017)

Basics of linear algebra (vector spaces and linear maps, solutions of linear equations, matrices).
Course completion requirements -
Last update: Andrew Goodall (11.10.2017)

To pass the tutorial, the student should obtain a minimum of 60% of points on the weekly quizzes and weekly homework combined.

Literature -
Last update: Andrew Goodall (11.10.2017)

D. Poole. Linear Algebra, A Modern Introduction. 3rd Int. Ed., Brooks Cole, 2011. Chapters 1,2,3,6.

Also useful:

G. Strang. Linear algebra and its applications. Thomson, USA, 4rd edition, 2006.

C. D. Meyer. Matrix analysis and applied linear algebra. SIAM, Philadelphia, PA, 2000.

W. Gareth. Linear Algebra with Applications. Jones and Bartlett Publishers, Boston, 4th edition, 2001.

R. Beezer, A First Course in Linear Algebra - a free online textbook. http://linear.ups.edu/html/fcla.html

Requirements to the exam -
Last update: Andrew Goodall (11.10.2017)

Pavel Valtr:

There is an examination consisting of a written and oral part.

The written part involves several exercises, taking approximately 60-90 minutes to complete satisfactorily.

The oral part involves discussion of solutions to the set problems and additional questions on topics covered in lectures and classes.

Class credits ("zapocet") are prerequisite for taking the examination.

Syllabus -
Last update: Andrew Goodall (11.10.2017)

Basic matrix operations, inverse matrices. Gaussian elimination, row echelon form, solving systems of linear equations.

Vector spaces: basic concepts, basis, dimension, linear mapping. Applications of linear algebra.

 
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