Teaching

Corso di Teoria Algebrica dei Numeri 19/20

Invito tutti gli studenti di Teoria Algebrica dei Numeri per l'AA 2019/2020 ad iscriversi alla seguente pagina moodle del presente corso:

https://didatticaonline.unitn.it/dol/course/view.php?id=19648

Su tale pagina pubbliccherò gli aggiornamenti relativi alle possibili modalità di svolgimento delle lezioni non appena saranno rese note.

[Pubblicato il 26 Febbraio 2020]

Teaching

Geometria e algebra lineare

The aim of the course is to provide the basic notions, theoretical and computational, of linear algebra and analytic geometry in the plane and in the space. At the end of the course the student will be able to work with vector spaces, linear systems, matrices, linear functions, eigenvalues and eigenvectors, besides with straight-lines and planes in the space in terms of their parametric and Cartesian representations.
The knowledge of the basics of analytic geometry, vector algebra and descriptive geometry, in all aspects directly or indirectly related to the identification of geometric shapes on the plane and in space.

Department of Civil, Environmental and Mechanical Engineering

2009 Ingegneria civile (LT) - standard (Esse3)

Geometria e algebra lineare

The aim of the course is to provide the basic notions, theoretical and computational, of linear algebra and analytic geometry in the plane and in the space. At the end of the course the student will be able to work with vector spaces, linear systems, matrices, linear functions, eigenvalues and eigenvectors, besides with straight-lines and planes in the space in terms of their parametric and Cartesian representations.
The knowledge of the basics of analytic geometry, vector algebra and descriptive geometry, in all aspects directly or indirectly related to the identification of geometric shapes on the plane and in space.

Department of Civil, Environmental and Mechanical Engineering

2019 Ingegneria per l'ambiente e il territorio (LT) - standard (Esse3)

Geometria e algebra lineare

The aim of the course is to provide the basic notions, theoretical and computational, of linear algebra and analytic geometry in the plane and in the space. At the end of the course the student will be able to work with vector spaces, linear systems, matrices, linear functions, eigenvalues and eigenvectors, besides with straight-lines and planes in the space in terms of their parametric and Cartesian representations.
The knowledge of the basics of analytic geometry, vector algebra and descriptive geometry, in all aspects directly or indirectly related to the identification of geometric shapes on the plane and in space.

Department of Civil, Environmental and Mechanical Engineering

2008 Ingegneria per l'ambiente e il territorio (LT) - standard (Esse3)

Tensor Decomposition for Big Data Analysis

An introduction to big data science from the point of view of tensor decomposition.The course will begin with concrete examples of big data problems. The central part of the course will be based on geometric structures for modeling the extraction of information from problems of large data collections. Part of the course will be devoted to computational aspects.1. Knowledge and understanding skills.Good knowledge of the basic arguments of tensor decomposition from the geometric point of view and concrete examples of big data.2. Ability to apply knowledge and understanding.Inductive and deductive reasoning ability to deal with issues that are provided individually or in a group from time to time.3. Autonomy of judgment.Ability to develop logical arguments and produce correct demonstrations. Ability to identify the most appropriate methods for analyzing, interpreting, and modeling information extraction issues from large data collections.4. Communicative Skills.Ability to expose subjects both at the written / computational level by carrying out exercises handed out by the instructor both at the oral level in the possible presentation of a topic taught at a lecture through a public seminar.

Department of Mathematics

2009 Matematica (LM) - Mathematics for Life and Data Sciences (Esse3)

Tensor Decomposition for Big Data Analysis

An introduction to big data science from the point of view of tensor decomposition.The course will begin with concrete examples of big data problems. The central part of the course will be based on geometric structures for modeling the extraction of information from problems of large data collections. Part of the course will be devoted to computational aspects.1. Knowledge and understanding skills.Good knowledge of the basic arguments of tensor decomposition from the geometric point of view and concrete examples of big data.2. Ability to apply knowledge and understanding.Inductive and deductive reasoning ability to deal with issues that are provided individually or in a group from time to time.3. Autonomy of judgment.Ability to develop logical arguments and produce correct demonstrations. Ability to identify the most appropriate methods for analyzing, interpreting, and modeling information extraction issues from large data collections.4. Communicative Skills.Ability to expose subjects both at the written / computational level by carrying out exercises handed out by the instructor both at the oral level in the possible presentation of a topic taught at a lecture through a public seminar.

Department of Mathematics

2018 Data science (LM) - standard (Esse3)

Tensor Decomposition for Big Data Analysis

An introduction to big data science from the point of view of tensor decomposition.The course will begin with concrete examples of big data problems. The central part of the course will be based on geometric structures for modeling the extraction of information from problems of large data collections. Part of the course will be devoted to computational aspects.1. Knowledge and understanding skills.Good knowledge of the basic arguments of tensor decomposition from the geometric point of view and concrete examples of big data.2. Ability to apply knowledge and understanding.Inductive and deductive reasoning ability to deal with issues that are provided individually or in a group from time to time.3. Autonomy of judgment.Ability to develop logical arguments and produce correct demonstrations. Ability to identify the most appropriate methods for analyzing, interpreting, and modeling information extraction issues from large data collections.4. Communicative Skills.Ability to expose subjects both at the written / computational level by carrying out exercises handed out by the instructor both at the oral level in the possible presentation of a topic taught at a lecture through a public seminar.

Department of Mathematics

2009 Matematica (LM) - Mathematics and Statistics for Life and Social Sciences (Esse3)

Teoria algebrica dei numeri

An introduction to number theory. The substantial part of the course will be focused to establish the foundations of algebraic number theory. We'll start with the theory of the elemental references numbers and end up with notes on geometrical aspects of modern number theory.1. Knowledge and understandingGood knowledge of the basic arguments of elementary and algebraic number theory.2. Ability to apply knowledge and understandingInductive and deductive reasoning skills in tackling problems provided from time to time both individually and in groups.3. Making judgmentsAbility to develop logical arguments and produce correct proofs. Ability to identify the most suitable methods to analyze and interpret problems.4. Communication skillsAbility to present arguments at both written by solving exercises as assigned by the teacher is the oral level possible in a place subject to class exposure through a public seminar.

Department of Mathematics

2008 Matematica (LT) - Scienze Matematiche (Esse3)

Consulting Hours

Where:
ZOOM room

When:
16-17

For the entire duration of the lessons in online mode, I am available in the virtual reception room ZOOM for Environmental and Civil Engineering on Fridays from 16 to 17. Refer to the Moodle page of the course for access credentials.

Last updated Thursday 12 March 2020 - 09:46