Tensor Decomposition for Big Data Analysis 2020/2021
All the information on the present course will be available at the Moodle community:
Tensor Decomposition for Big Data Analysis [145909]  BERNARDI
Teaching
 Advanced Commutative Algebra

The commutative algebra is an extremely versatile discipline. In addition to being itself a field of research in mathematics both classical and contemporary, it has important consequences both in computational framework that on the foundations of modern algebraic geometry, not to mention the recent uses in the application environment.The purpose of this course is to provide a good understanding of the foundations of modern commutative algebra with a particular focus on computational methods and its use in algebraic geometry.The final part of the course will introduce students to the most recent applications.
Department of Mathematics
2009 Matematica (LM)  Advanced Mathematics (Esse3)
 Geometria e algebra lineare con elementi di statistica

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 also in MATLAB with vector spaces, linear systems, matrices, linear functions, eigenvalues and eigenvectors, besides with straightlines and planes in the space in terms of their parametric and Cartesian representations.  They will know the basic language and concepts of Probability Calculation and will be able to use them to describe and model simple models of random experiments. They will know the basic concentrates relating to random variables and will be able to illustrate and apply them in the measurement of quantities and in the collection of statistical data. They will know intuitively, through examples and without a formal presentation, the properties of the sample mean, the law of large numbers, the central theorem of the limit. They will be able to apply the knowledge acquired to simple problems of both point estimation and interval between statistical inference.
Department of Civil, Environmental and Mechanical Engineering
2020 Ingegneria civile (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 and Statistics for Life and Social Sciences (Esse3)
 Tensor Decomposition for Big Data Analysis

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)
Consulting Hours
ZOOM room
1617
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.