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 !! low !! high |
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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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| + | |} |
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Revision as of 13:00, 25 March 2022
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?
This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines. Linear algebra is a branch of mathematics that studies systems of linear equations and the properties of matrices. The concepts of linear algebra are extremely useful in physics, data sciences, and robotics. Due to its broad range of applications, linear algebra is one of the most widely used subjects in mathematics.
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 | low | high |
|---|---|---|
| A | 85 | 100 |
| B | 65 | 84 |
| C | 50 | 64 |
| D | 0 | 49 |