CODE 98959 ACADEMIC YEAR 2024/2025 CREDITS 6 cfu anno 2 INGEGNERIA MECCANICA - PROGETTAZIONE E PRODUZIONE 9269 (LM-33) - LA SPEZIA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE English TEACHING LOCATION LA SPEZIA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW The course provides basic knowledge of classic and modern machine learning techniques, that can fruitfully be applied to disparate fields such as production line automation, quality monitoring, robotics, surveillance, self-driving vehicles, among many others. AIMS AND CONTENT LEARNING OUTCOMES This course provides an introduction to machine learning and statistical pattern recognition. Topics include: (1) Pattern recognition basics and theory. (2) Supervised learning ideas and methods. (3) Unsupervised learning ideas and some relevant methods. (4) Machine learning workflows and best practices. The course will also cover relevant success stories, and possible applications and case studies in the fields of robotics and smart industrial automation AIMS AND LEARNING OUTCOMES After successfully attending the course, the student will be able to: demonstrate knowledge of a range of techniques and problems in machine learning and pattern recognition, including the underlying scientific and technical rationale apply selected techniques to relevant problems code simple and medium-complexity machine learning methods using standard programming tools, without being limited to using software libraries tackle the workflow of a machine learning assignment from data wrangling to result presentation use critical thinking to analyse a problem and select the appropriate machine learning method to apply PREREQUISITES Basic knowledge of calculus, linear algebra, geometry, which are typically acquired during the first or second year in any engineering curriculum Basic, but operational, knowledge of Matlab or Python programming More basic topics (elements of probability, of statistics, of optimisation) will be covered during the course TEACHING METHODS Lectures, guided labs, homework assignments SYLLABUS/CONTENT Introduction, basic concepts, types of problems Linear thershold classifiers Probabilities; Bayesian decision theory (the Naive bayes classifier) Linear regression as a simple learning problem Optimisation (convexity, criteria, gradient descent, stochastic methods) Statistics and learning (sampling, parameter estimation) Parametric and non-parametric methods (Gaussian mixtures, nearest neighbour rules, decision trees/forests) Evaluation of classifiers (methodology, quality indices) Neural networks (history, perceptrons, multilayer perceptrons, the error back-propagation algorithm, deep learning) Unsupervised learning (clustering methods) Mapping and input space transformations (PCA, nonlinear embedding methods, kernel methods, support vector machines) RECOMMENDED READING/BIBLIOGRAPHY Course slides/handouts For a detailed bibliograpy please refer to the course Aulaweb page (from https://corsi.unige.it/9269#chapter-5 open Manifesto degli Studi, look up Machine Learning and click it) TEACHERS AND EXAM BOARD STEFANO ROVETTA Ricevimento: All lecture days after class (approx. 20 min). Upon prior agreement, at any other time. Since the teacher is in charge of several courses, if you are getting in touch with means other than in person (email, Teams messaging, Aulaweb mesaging...) please specify which degree and which course you are referring to. LESSONS LESSONS START https://corsi.unige.it/en/corsi/9269 Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Written quiz, homework assignments ASSESSMENT METHODS Evaluation of homeworks Homeworks address the following learning outcomes: apply selected techniques to relevant problems code simple and medium-complexity machine learning methods using standard programming tools, without being limited to using software libraries tackle the workflow of a machine learning assignment from data wrangling to result presentation Quiz grading Quizzes address the following learning outcomes: demonstrate knowledge of a range of techniques and problems in machine learning and pattern recognition, including the underlying scientific and technical rationale use critical thinking to analyse a problem and select the appropriate machine learning method to apply