Unit DATA SCIENCE
- Course
- Engineering management
- Study-unit Code
- A005324
- Curriculum
- In all curricula
- CFU
- 15
- Course Regulation
- Coorte 2025
- Offered
- 2025/26
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa integrata
INDUSTRIAL AND BUSINESS STATISTICS
| Code | A005325 |
|---|---|
| CFU | 6 |
| Teacher | Nicola Senin |
| Teachers |
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| Hours |
|
| Learning activities | Caratterizzante |
| Area | Ingegneria gestionale |
| Sector | ING-IND/16 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | English |
| Contents | The module illustrates the methods and challenges related to statistical modelling and analysis of business operations, covering supply chain, production, marketing, distribution and sales. Specifically for manufacturing, the module focuses on quality control and management, introducing methods for statistical process monitoring and sampling inspection. |
| Reference texts | Instructor handouts |
| Educational objectives | Acquire the capability to apply statistical modelling and analysis methods to analyse the performance of business operations and to perform quality management and control in industrial production |
| Prerequisites | No prerequisite modules. Some knowledge of basic statistics and data analysis are preferable. |
| Teaching methods | Frontal lectures |
| Other information | NA |
| Learning verification modality | Final exam |
| Extended program | Part I – Quality in industrial production Introduction to the concept of quality in industrial production: conformance to specifications, absence of defects, product features. Quality control and quality management strategies. Total quality management. ISO 9000. Part II – Statistical process monitoring and control Industrial production as a random process: process variability, process capability. Statistical modelling of process variables and attributes. Statistical process monitoring. Control charts for variables and for attributes. Process recovery and improvement: six-sigma, DMAIC. Taguchi methods. Part III – Statistical modelling of inspection Introduction to inspection and testing. Outcomes of binary inspection: confusion matrix, type 1 and type 2 errors in inspection. 100% vs sampling inspection. Sampling inspection for variables and for attributes. Types of inspection solutions and their use in manufacturing Part IV – Statistical modelling of other business operations Statistical modelling and analysis of the supply chain. Analysis of business operations in addition to production (e.g., marketing and distribution). Modelling and analysis of sales. Analysis of correlations between sales and operations. Customer spending and behaviour analysis. |
| Obiettivi Agenda 2030 per lo sviluppo sostenibile | Objectives 8,9 and 12 |
MACHINE LEARNING
| Code | A005326 |
|---|---|
| CFU | 9 |
| Teacher | Gabriele Costante |
| Teachers |
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| Hours |
|
| Learning activities | Caratterizzante |
| Area | Ingegneria gestionale |
| Sector | ING-INF/04 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | ENGLISH |
| Contents | Introduction to Machine Learning. Linear Regression. Discriminative and Generative Models for classification. Support Vector Machines. Neural Network. Unsupervised Learning approaches. Application examples. Guided laboratory exercises on data analysis with Machine Learning techniques. |
| Reference texts | 1. "The Elements of Statistical Learning", T. Hastie, R. Tibishirani, J. Friedman, Springer (free) 2. "Pattern Recognition and Machine Learning", C. M. Bishop, Springer 3. "An Introduction to Statistical Learning, with application in R", G. James, D. Witten, T. Hastie, R. Tibshirani, Springer (free) 4. "Python Machine Learning", S. Raschka, PACKT Publishing 5. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge Univ. Press, 2000 6. R.O. Duda, P.E. Hart,D.G. Stork, Pattern Classification, 2nd ed., Wiley, 2012 Dispense a cura del docente disponibili su UNISTUDIUM - PIATTAFORMA DI E-LEARNING DELL'UNIVERSITÀ DEGLI STUDI DI PERUGIA (https://www.unistudium.unipg.it/unistudium/ ) |
| Educational objectives | Knowlendge of the main problems of the field and the main algorithm to solve them. Practical skills in doing Exploratory Data Analysis. Practical skills in developing and debugging Machine Learning applications. Knowledge of the main library for Machine Learning and Data Analysis. |
| Prerequisites | In order to understand the content presented and achieve the learning objectives it is useful to have a good knowledge of Linear Algebra, Probability and basic Calulus. Basic programming skills in any programming language. |
| Teaching methods | The teaching is organized as follows:- frontal lectures in the classroom;- seminars- guided exercises at the Computer Science Laboratory on the design of convolutional neural networks with the Pytorch framework. During laboratory lectures, the students are distributed on 30 workstations. Teaching support tools: blackboard and PC+projector, PC. |
| Other information | I Semester (more details at link http: //www.ing.unipg.it/it/didattica/studiare-nei-nostri-corsi/orario-delle-lezioni ). |
| Learning verification modality | The verification of learning is performed with an oral exam and a practical test (project). The oral test includes a discussion of the submitted project and two questions on topics chosen by the teacher, and lasts about 45 minutes. The exam aims to verify the student's understanding of the fundamental theoretical tools provided by the teaching, as well as the relationship between these tools and the general topics of information engineering. The project is used to verify the ability to use Machine Learning techniques for the analysis of data on real problems. Reservations for examinations are made via the SOL portal: https://www.segreterie.unipg.it/. For information on support services for students with disabilities and/or DSA visit http://www.unipg.it/disabilita-e-dsa. |
| Extended program | Introduction to the Machine Learning field and applications Elements of Convex Optimization Linear Regression CLassification (Logistic Regression, KNN, LDA, QDA) Model selection Support Vector Machines Neural Networks Generative models (Bayes Classifiers) Unsupervised Learning (K-means, K-medoids, EM, Factor Analysis, PCA, ICA) Applications |
| Obiettivi Agenda 2030 per lo sviluppo sostenibile |