01TXGSM

A.A. 2020/21

Course Language

Inglese

Degree programme(s)

Master of science-level of the Bologna process in Data Science And Engineering - Torino

Course structure

Teaching | Hours |
---|---|

Lezioni | 60 |

Esercitazioni in aula | 20 |

Lecturers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
---|---|---|---|---|---|---|---|

Vaccarino Francesco | Professore Associato | MAT/03 | 30 | 10 | 0 | 0 | 5 |

Co-lectuers

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

MAT/03 SECS-S/01 |
4 4 |
C - Affini o integrative F - Altre attività (art. 10) |
Attività formative affini o integrative Altre conoscenze utili per l'inserimento nel mondo del lavoro |

2020/21

The aim of this course is to introduce the students to a solid mathematical foundation of Machine Learning (ML) by blending learning theory, geometry, topology and statistics. Starting from introducing the algebraic and geometric structures used to represent and manipulate data, we will move to the more geometrical aspects of ML with a particular attention to the various concepts of dimension and learnability. Linear algebra-based methods will be thoroughly presented. At the same time, (generalized) linear models, their selection, regularization, validation and hyperparametric tuning will be presented in full detail from a rigorous statistical point of view as well as some Bayesian methods. These two aspects of the theory, the geometrical and the statistical one, will merged via case study on real and in silico data.

The aim of this course is to introduce the students to a solid mathematical foundation of Machine Learning (ML) by blending learning theory, geometry, topology and statistics. Starting from introducing the algebraic and geometric structures used to represent and manipulate data, we will move to the more geometrical aspects of ML with a particular attention to the various concepts of dimension and learnability. Linear algebra-based methods will be thoroughly presented. At the same time, (generalized) linear models, their selection, regularization, validation and hyperparametric tuning will be presented in full detail from a rigorous statistical point of view as well as some Bayesian methods. These two aspects of the theory, the geometrical and the statistical one, will merged via case study on real and in silico data.

The student will learn the basic concepts of machine and statistical learning from both the frequentist and the Bayesian viewpoint, the main techniques for multivariate data and the critical use of specialised software (R, SAS, BUGS, STAN, MATLAB, ORANGE, R, Python, Rapid Miner and the like), being able to tell the pros and cons.

The student will learn the basic concepts of machine and statistical learning from both the frequentist and the Bayesian viewpoint, the main techniques for multivariate data and the critical use of specialised software (R, SAS, BUGS, STAN, MATLAB, ORANGE, R, Python, Rapid Miner and the like), being able to tell the pros and cons.

Knowledge of basic probability theory and statistics; linear algebra, in particular SVD; basic of metric geometry and calculus are the prerequisites for this course.

Knowledge of basic probability theory and statistics; linear algebra, in particular SVD; basic of metric geometry and calculus are the prerequisites for this course.

• Mathematical representations of data: spaces (including Hilbert spaces), metrics, distances, dissimilarities and kernels. Geometry of very high dimensional spaces and the curse of dimensionality.
• Learning theory, PAC, Rademacher and VC dimension. Trade-off Bias vs Model Variance and Model Complexity.
• Cross validation, bootstrap and applications.
• Linear algebra-based methods: Principal Component Analysis, Linear Discriminant Analysis, Independent Component Analysis and Stochastic projections (Johnson - Lindenstrauss Transform).
• Linear Models (regression, ANOVA, DOE).
• Generalized linear models (categorical data, logistic and multinomial regression).
• Model and feature selection, hyperparameter tuning (e.g. lasso, AIC, BIC, ridge).
• Bayesian networks (basic concepts, exact and MCMC-based computations).

• Mathematical representations of data: spaces (including Hilbert spaces), metrics, distances, dissimilarities and kernels. The geometry of very high dimensional spaces and the curse of dimensionality.
• Learning theory, PAC and VC dimension. Trade-off Bias vs Model Variance and Model Complexity.
• Cross-validation, bootstrap and applications.
• Linear algebra-based methods: Principal Component Analysis, Linear Discriminant Analysis, Independent Component Analysis and Stochastic projections (Johnson - Lindenstrauss Transform).
• Linear Models (regression, ANOVA, DOE).
• Generalized linear models (categorical data, logistic and multinomial regression).
• Model and feature selection, hyperparameter tuning (e.g. lasso, AIC, BIC, ridge).
• Bayesian networks (basic concepts, exact and MCMC-based computations).

In the first part of the course the lectures are held with the support of slides. Exercises are presented and solved in the class as well. In the final part of the course the lessons will mainly consist in activities carried out at the computer lab under the guidance of the teacher. Technical discussions during class lectures will also help to assess the acquired level of knowledge and ability at the different stages of the course.

In the first part of the course the lectures are held with the support of slides. Exercises are presented and solved in the class as well. In the final part of the course the lessons will mainly consist in activities carried out at the computer lab under the guidance of the teacher. Technical discussions during class lectures will also help to assess the acquired level of knowledge and ability at the different stages of the course.

Slides of the lectures, examples of R and python scripts and exercises with solutions will be available in the website of the course. A list of suggested books will be also provided by the teacher during the first lecture.

Slides of the lectures, examples of R and python scripts and exercises with solutions will be available on the website of the course.
A list of suggested books:
- An Introduction to Statistical Learning with Applications in R.
James, G., Witten, D., Hastie, T., Tibshirani, R.
Springer Verlag
- Understanding machine learning: From theory to algorithms.
Shalev-Shwartz, Shai, and Shai Ben-David.
Cambridge university press, 2014.

The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analyzing data using the methods explained in the course.
The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analysing data using the methods explained in the course. The exam consists of two parts: first, the candidate will write a technical relation "tesina" on the analysis of a data set performed by using the methods taught in the course. Once the "tesina" is approved by the professor, then the student is allowed to present it in an oral exam (about 15.min) during which the professor will also ask questions on the theoretical aspects of the methods used in the tesina.

The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analyzing data using the methods explained in the course.
The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analysing data using the methods explained in the course. The exam consists of two parts: first, the candidate will write a short technical report "tesina" on the analysis of a data set performed by using the methods taught in the course. Once the "tesina" is approved by the professor, then the student is allowed to present it in an oral exam (about 15.min) during which the professor will also ask questions on the theoretical aspects.

The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analyzing data using the methods explained in the course.
The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analysing data using the methods explained in the course. The exam consists of two parts: first, the candidate will write a technical relation "tesina" on the analysis of a data set performed by using the methods taught in the course. Once the "tesina" is approved by the professor, then the student is allowed to present it in an oral exam (about 15.min) during which the professor will also ask questions on the theoretical aspects of the methods used in the tesina.

The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analyzing data using the methods explained in the course.
The goal of the exam is to test the knowledge of the candidate about the topics included in the official program and to test their skills in analysing data using the methods explained in the course. The exam consists of two parts: first, the candidate will write a short technical relation "tesina" on the analysis of a data set performed by using the methods taught in the course. Once the "tesina" is approved by the professor, then the student is allowed to present it in an oral exam (about 15.min) during which the professor will also ask questions on the theoretical aspects.

© Politecnico di Torino

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY