Servizi per la didattica
PORTALE DELLA DIDATTICA

Machine learning for networking

01DSMBG

A.A. 2022/23

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Communications Engineering - Torino

Course structure
Teaching Hours
Lezioni 30
Esercitazioni in laboratorio 50
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Cumani Sandro   Professore Associato ING-INF/05 30 0 0 0 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/05 8 C - Affini o integrative Attività formative affini o integrative
2022/23
The course aims at providing a solid introduction to machine learning, a branch of artificial intelligence that deals with the development of algorithms able to extract knowledge from data, with a focus on pattern recognition and classification problems. The course will cover the basic concepts of statistical machine learning, both from the frequentist and the Bayesian perspectives, and will be focused on the broad class of generative linear Gaussian models and discriminative classifiers based on logistic regression and support vector machines. The objective of the course is to provide the students with solid theoretical bases that will allow them to select, apply and evaluate different classification methods on real tasks. The students will also acquire the required competencies to devise novel approaches based on the frameworks that will be presented during the classes. The course will include laboratory activities that will allow the students to practice the theoretical notions on real data using modern programming frameworks that are widely employed both by research communities and companies.
The course will provide an overview of machine learning approaches and tools, focusing on application of machine learning algorithms to address inference problems in the field of communication networks. The course will cover the basic concepts of machine learning and data analysis, concentrating mainly on three classes of problems: clustering, classification and regression. The objective of the course is to provide the students with the skills to employ existing machine learning algorithms and libraries to solve real application problems. The students will also acquire Python programming competencies. A significant part of the courses will be devoted to laboratory activities that will allow the students to practice the theoretical notions on real problems using modern programming frameworks that are widely employed both by the research communities and companies.
At the end of the course the students will - know and understand the basic principles of statistical machine learning applied to pattern recognition and classification; - know the principal techniques for classification, including generative linear Gaussian models and discriminative approaches based on logistic regression and support vector machines, among others; - understand the theoretical motivations behind different classification approaches, their main properties and domain of application, and their limitations; - be able to implement the different algorithms using wide-spread programming frameworks (Python) - be able to apply different methods to real tasks, to critically evaluate their effectiveness and to analyze which strategies are better suited to different applications; - be able to transfer the acquired knowledge and capabilities to solve novel classification problems, developing novel methods based on the frameworks that will be discussed during classes
At the end of the course the students will: - know and understand the basic principles of machine learning; - know the principal techniques for classification, regression and clustering; - understand the main theoretical properties, domains of application, and limitations of different machine learning approaches; - know and be able to employ the Python programming language and the main Python libraries for machine learning; - be able to employ the Python machine learning libraries to devise complete solutions for inference problems; - be able to apply different methods to real tasks, to critically evaluate their effectiveness and to analyze which strategies are better suited to different applications; - be able to transfer the acquired knowledge and capabilities to solve novel problems.
The students should have basic knowledge of probability and statistics, linear algebra and calculus.
The students should have basic knowledge of probability and statistics, linear algebra, calculus and programming.
Machine learning and pattern recognition - Introduction and definitions Probability theory concepts - Random Variables - Estimators - The Bayesian framework Introduction to Python - The language - Main numerical libraries Decision Theory - Inference, expected loss - Model taxonomy: generative and discriminative approaches - Model optimization, hyperparameter selection, cross-validation Model evaluation - Classification scores and log-likelihood ratios - Detection Cost Functions and optimal Bayes decisions Dimensionality reduction - Principal Component Analysis (PCA) - Linear Discriminant Analysis (LDA) Generative Gaussian models - Generative Gaussian classifiers: univariate Gaussian, Naive Bayes, multivariate Gaussian (MVG) - Tied covariance MVG and LDA Logistic Regression (LR) - From Tied MVG to LR - LR as ML solution for class labels - Binary and multiclass cross-entropy - From MVG to Quadratic LR - LR as empirical risk minimization - Overfitting and regularization Support Vector Machines (SVM) - Optimal classification hyperplane: the maximum margin definition - Margin maximization and L2 regularization - SVM as minimization of classification errors - Primal and dual SVM formulation - Non linear extension: brief introduction to kernels Density estimation and latent variable models - Gaussian mixture models (GMM) - The Expectation Maximization algorithm Continuous latent variable models: Linear-Gaussian Models - Linear regression - Linear regression and Tied MVG - MVG with unknown class means: Probabilistic LDA (PLDA) - Bayesian MVG - Factor Analysis: PLDA, Probabilistic PCA Approximated inference basics - Variational Bayes
Python and Machine Learning tools (1.5 cfu) - The Python language - Numerical libraries - Machine Learning libraries Introduction to Machine Learning (0.3 cfu) - Definitions and taxonomy of Machine Learning tasks Dimensionality reduction (0.7 cfu) - Principal Component Analysis (PCA) - Linear Discriminant Analysis (LDA) Machine Learning for Clustering (1 cfu) - Agglomerative Hierarchical Clustering - K-Means - DBSCAN Machine Learning for Classification (3.5 cfu) - Gaussian models and generative classifiers - Logistic Regression - Support Vector Machines - Decisions Trees - Gaussian mixture models (GMM) Machine Learning for Regression (1 cfu) - Linear Regression - Support Vector Regression
The course will include 3 hours of lectures and 1,5 hours of laboratory per week. The lectures will focus both on theoretical and practical aspects, and will include open discussions aimed at developping suitable solutions for different problems. The laboratories will allow the students to implement most of the techniques that will be presented during the lectures, and to apply the learned methods to real data.
The course will include 30 hours of lectures and 50 hours of laboratory. The lectures will focus both on theoretical and practical aspects, and will include open discussions aimed at developing suitable solutions for different problems. The laboratories will allow the students to apply the methods presented during lectures to real data, with a particular focus on networking applications.
[1] Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg. [2] Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press. Additional material, including slides and code fragments, will be made available on the course website.
[1] Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg. [2] Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press. Additional material, including slides and code fragments, will be made available on the course website.
Modalità di esame: Prova scritta (in aula); Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Written test; Individual project; Group project;
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Written test; Individual project; Group project;
The exam will assess the knowledge of the course topics, and the ability of the candidate to apply such knowledge and the developed skills to solve specific problems. The exam will consist in two parts: - A project to be developed during the course. The students will be able to choose individual or (small) group projects among a set of possible choices (max. 18 points). - A written examination (max. 12 points). The final mark will be the sum of the report and written exam marks. To pass the exam, the report mark must be at least 9/18, the written exam mark must be at least 6/12, and the final mark must be at least 18/30. The projects will address machine learning tasks. For each project, a dataset will be provided, and the students will have to develop suitable models for the specific task based on the topics and tools presented during lectures and laboratories. Each candidate will have to provide a technical report detailing the employed methodology and a critical analysis of the obtained results. The report will assess: - The degree of understanding of the theoretical principles of different machine learning approaches - The ability of the student to analyze a specific problem, assessing which approaches, among those that have been presented, are more suited to solve the task - The ability of the student to apply the studied methods to devise suitable solutions for the specific case study - The ability of the student to critically evaluate the effectiveness of the proposed approaches. The written examination will consists of open questions covering the topics presented during the lectures. The written examination will assess: - The theoretical understanding of the basic principles of the presented machine learning approaches - The knowledge and understanding of the different approaches that have been presented during the lectures - The ability of the student to critically analyze and evaluate the different approaches.
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.
Esporta Word


© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti