Study-unit MACHINE LEARNING AND DATA ANALYSIS
| Course name | Computer engineering and robotics |
|---|---|
| Study-unit Code | A002336 |
| Curriculum | Comune a tutti i curricula |
| Lecturer | Gabriele Costante |
| Lecturers |
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| Hours |
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| CFU | 9 |
| Course Regulation | Coorte 2023 |
| Supplied | 2023/24 |
| Supplied other course regulation | |
| Learning activities | Caratterizzante |
| Area | Ingegneria informatica |
| Sector | ING-INF/04 |
| Type of study-unit | Obbligatorio (Required) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | Italian |
| 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 Machine Learning and applications Linear Regression CLassification (Logistic Regression, KNN, LDA, QDA) Generative models (LDA, Bayes Classifiers) Decision Trees and Random Forests Model selection Elements of Convex Optimization Support Vector Machines Neural Networks Unsupervised Learning (K-means, EM, PCA, ICA) Applications |
| Obiettivi Agenda 2030 per lo sviluppo sostenibile |