Difference between revisions of "IU:TestPage"
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* Fast Fourier Transform |
* Fast Fourier Transform |
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* To find eigenvalues and eigenvectors for matrix diagonalization and single value decomposition |
* To find eigenvalues and eigenvectors for matrix diagonalization and single value decomposition |
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+ | === Course evaluation === |
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+ | {| class="wikitable" |
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+ | |+ Course grade breakdown |
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+ | |- |
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+ | ! Type !! Points |
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+ | |- |
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+ | | Labs/seminar classes || 20 |
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+ | |- |
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+ | | Interim performance assessment || 30 |
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+ | |- |
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+ | | Exams || 50 |
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+ | |} |
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+ | |||
+ | === Grades range === |
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+ | {| class="wikitable" |
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+ | |+ Course grading range |
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+ | |- |
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+ | ! Grade !! Points |
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+ | |- |
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+ | | A || [85, 100] |
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+ | |- |
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+ | | B || [65, 84] |
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+ | |- |
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+ | | C || [50, 64] |
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+ | |- |
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+ | | D || [0, 49] |
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+ | |} |
Revision as of 16:03, 6 December 2021
Analytical Geometry \& Linear Algebra -- II
- Course name: Analytical Geometry \& Linear Algebra -- II
- Course number: XYZ
Course Characteristics
Key concepts of the class
- fundamental principles of linear algebra,
- concepts of linear algebra objects and their representation in vector-matrix form
What is the purpose of this course?
Course objectives based on Bloom’s taxonomy
- What should a student remember at the end of the course?
By the end of the course, the students should be able to
- List basic notions of linear algebra
- Understand key principles involved in solution of linear equation systems and the properties of matrices
- Linear regression analysis
- Fast Fourier Transform
- How to find eigenvalues and eigenvectors for matrix diagonalization and single value decomposition
- What should a student be able to understand at the end of the course?
By the end of the course, the students should be able to
- Key principles involved in solution of linear equation systems and the properties of matrices
- Become familiar with the four fundamental subspaces
- Linear regression analysis
- Fast Fourier Transform
- How to find eigenvalues and eigenvectors for matrix diagonalization and single value decomposition
- What should a student be able to apply at the end of the course?
By the end of the course, the students should be able to
- Linear equation system solving by using the vector-matrix approach
- Make linear regression analysis
- Fast Fourier Transform
- To find eigenvalues and eigenvectors for matrix diagonalization and single value decomposition
Course evaluation
Type | Points |
---|---|
Labs/seminar classes | 20 |
Interim performance assessment | 30 |
Exams | 50 |
Grades range
Grade | Points |
---|---|
A | [85, 100] |
B | [65, 84] |
C | [50, 64] |
D | [0, 49] |