Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea in Informatica - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2012/2013
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3° Year activated in the A.Y. 2013/2014
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Un insegnamento a scelta tra i seguenti:
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Un insegnamento a scelta tra i seguenti:
Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Linear Algebra (2011/2012)
Teaching code
4S00002
Teacher
Coordinator
Credits
6
Also offered in courses:
- Linear Algebra of the course Bachelor's degree in Bioinformatics
Language
Italian
Scientific Disciplinary Sector (SSD)
MAT/02 - ALGEBRA
Period
I semestre dal Oct 3, 2011 al Jan 31, 2012.
Learning outcomes
Introduction to the fundaments of Linear Algebra with some applications.
Program
* Matrices and linear systems: matrices, operations on matrices, systems of linear equations, Gauss elimination, inverses of matrices, LU factorization.
* Vector spaces: definition and examples, subspaces, generators. Linearly dependent and independent vectors, bases, dimension.
* Linear maps and associated matrices: composition of linear maps and matrix multiplication, change of basis, kernel and image of a linear map, rank of matrices, dimension formula.
* Inner products and orthogonality: inner product of vectors, orthogonal and orthonormal bases, orthogonal projections, Gram-Schmidt algorithm.
* Canonical forms: eigenvalues and eigenvectors, characteristic polynomial, algebraic and geometric multiplicity, diagonalizability criteria.
Examination Methods
Written test