Unit MATHEMATICAL METHODS FOR ARTIFICIAL INTELLIGENCE
- Course
- Informatics
- Study-unit Code
- A002083
- Curriculum
- Artificial intelligence
- Teacher
- Gianluca Vinti
- CFU
- 12
- Course Regulation
- Coorte 2020
- Offered
- 2021/22
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa integrata
APPLIED IMAGE AND SIGNAL PROCESSING
| Code | A002085 |
|---|---|
| CFU | 6 |
| Teacher | Gianluca Vinti |
| Teachers |
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| Hours |
|
| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Academic discipline | MAT/05 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | English |
| Contents | • Introduction to the course •Summary of images and applications of the sampling theorem to image •Approximate sampling and sampling-Kantorovich operators: •MATLAB and its applications to Image Processing •GIMP, IMAGEJ, PHYTON and their applications to Image Processing •Medical diagnostics: -General introduction to medical diagnostics - Diagnostic and interventional vascular surgery •Applied Mathematics Lab •New diagnostic methods in ophthalmology -OCT, Angio OCT and new techniques. Maculopaties and retinopaties -Image Processing for the diagnosis •Hospital visits: -Visit to the radiology department: radiography (RX), Computer Tomography (CT), Magnetic resonance imaging (MRI), Echography, CardioCT -Vascular Surgery interventions: endovascular treatments -Innovative instrumentation for ophthalmic diagnosis -Clinical trials of a microwaves mammography and image processing •Non invasive diagnostic: applications -Image Processing for seismic vulnerability -Image Processing for thermal bridges -Use of thermography •Visit to the Centre for Research on pollution and environment -Hot-Box, thermography, thermal bridges and noise |
| Reference texts | Notes and slides of the teacher. It will be recommended some books. |
| Educational objectives | Learning outcomes: The course provides knowledge of the major concepts of image processing with particular reference to the medical ones. The main knowledge (descriptor Dublin 1) will be acquired: • knowledge of the reconstruction of signals and images by sampling; • Knowledge of the main concepts and analysis techniques and image processing; • knowledge of the main problems of medical diagnostics and application of algorithms for the improvement of image; The main skills acquired (ability to apply the knowledge gained descriptor Dublin 2, and to take with independent judgment the appropriate approach, the Dublin descriptor 3) will be: • analytical skills and image processing whose purpose is to medical diagnosis; • ability to develop a line of reasoning that leads the student to identify the methods of solving the problem in question; • ability to identify the right approach to the solution of the problem with an eye toward the medical diagnostics. |
| Prerequisites | Knowledge of reconstruction of signals and images by sampling; Knowledge of the main concepts and analysis techniques and image processing; Knowledge of the main problems of medical diagnostics and of the application of algorithms for the improvement of image. |
| Teaching methods | The course is organized as follows: 1) Lectures on all topics of the course; 2) Classroom exercises. 3) laboratory: No. 6 guided visits to four hours each at the Hospital Santa Maria della Misericordia, Perugia (sections: radiology, vascular surgery and ophthalmology). |
| Other information | Attendance is strongly recommended for all lessons. |
| Learning verification modality | Oral exam. The verification of the educational objectives of teaching (examination) include an oral exam. The oral exam will be held on the dates set out in the examinations of the CdS calendar. The oral examination, consists of a discussion of the duration not exceeding 40 minutes each aimed at verifying: i) the level of knowledge of the theoretical and laboratory course content (Dublin descriptor 1), ii) the level of expertise in exposing the logical and mathematical reasoning skills (descriptor Dublin 2), iii) the independence of judgment (Dublin descriptor 3) to propose the most appropriate approach to argue what is required. The oral examination also aims to verify the student's ability to present with properties of language questions proposed by the Commission, to support a dialectical relationship during discussion and dimostrate logical-deductive ability and synthetic exposition (descriptor Dublin 4). The final mark will be made by the Commission in thirty. |
| Extended program | • Introduction to the course •Summary of images and applications of the sampling theorem to image •Approximate sampling and sampling-Kantorovich operators: - Theory -Applications to reconstruction and enhancement of images - Implementation of the theory to Image processing •MATLAB and its applications to Image Processing •GIMP, IMAGEJ, PHYTON and their applications to Image Processing •Medical diagnostics: -General introduction to medical diagnostics - Diagnostic and interventional vascular surgery •Applied Mathematics Lab: -Preparation and image processing -Application of algorithms, evaluation of results and interpretation of errors •New diagnostic methods in ophthalmology -OCT, Angio OCT and new techniques. Maculopaties and retinopaties -Image Processing for the diagnosis •Hospital visits: -Visit to the radiology department: radiography (RX), Computer Tomography (CT), Magnetic resonance imaging (MRI), Echography, CardioCT -Vascular -Surgery interventions: endovascular treatments -Innovative instrumentation for ophthalmic diagnosis -Clinical trials of a microwaves mammography and image processing •Non invasive diagnostic: applications -Image Processing for seismic vulnerability -Image Processing for thermal bridges -Use of thermography •Visit to the Centre for Research on pollution and environment -Hot-Box, thermography, thermal bridges and noise. |
NUMERICAL METHODS FOR INFORMATION TECHNOLOGIES
| Code | A002084 |
|---|---|
| CFU | 6 |
| Teacher | Bruno Iannazzo |
| Teachers |
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| Hours |
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| Learning activities | Affine/integrativa |
| Area | Attività formative affini o integrative |
| Academic discipline | MAT/08 |
| Type of study-unit | Obbligatorio (Required) |
| Language of instruction | Italian or English (if all students agree). |
| Contents | Selected topics in Numerical Analysis related to approximation, linear algebra and non-linear optimization and their applications to data science and information technologies. |
| Reference texts | For the numerical analysis topics: J. Stoer, R. Bulirsch. Introduction to numerical analysis. Springer. 2013; material provided by the teacher. For the optional topics, we will use a specific reference, that will be communicated during the lectures. |
| Educational objectives | Get into mathematical aspects related to some important data science and information technologies applications. Getting in touch with all mathematical modeling aspects: abstraction, modelisting interpretation, computer simulation. |
| Prerequisites | Basics in numerical analysis. |
| Teaching methods | Face-to-face lectures and laboratory sessions, using Matlab/Octave |
| Learning verification modality | Oral exam and seminar. The oral exam can be substituted by some exercises, a project or a seminar, upon availability. |
| Extended program | Least square approximation and singular value decomposition (principal component analysis). QR factorization and Nonnegative matrix factorization. Matrices with positivity structure, eigenvalue computation. Non-linear and manifold optimization. During the course, some applications to data science and information technologies will be taken into account: data fitting, complex networks; classification problems; machine learning. Spline interpolation. B-splines. Trigonometric interpolation. Fast Fourier Transform. Application to digital filtering, curves and surfaces in computer graphics. |