Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati. - 9-12 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione - 6-9 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf)riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
- Insegnamento
- Documentazione
- Esame
Analyzing images is a very complex task that has many important real-world applications. This module presents powerful techniques to extract information from images and 3D data, based on machine learning and deep learning methods. These methods are mostly used as “black boxes” and their inner workings are not discussed in much detail. The module provides an overview of many image analysis applications such as document analysis, medical imaging and autonomous driving; examples of advanced uses of deep learning on images (generative networks for image synthesis, adversarial networks, neural style transfer) are also discussed.
Requisiti
Prerequisites:
- Basic knowledge of machine learning (e.g. Andrew Ng’s ML course on Coursera)
- Good command of an imperative programming language, basic knowledge of Python (the module will use Python 3).
- www.scipy-lectures.org/index.html Sections 1.1, 1.2, 1.3, 3.6.1, 3.6.2
- Basic knowledge of probability, statistics, linear algebra (vectors, matrices)
- Students are expected to take their laptops for the Lab activities
Obiettivi di apprendimento
- Students know how images and 3D data are represented and manipulated by software
- Students know the most important problems related to image analysis: e.g. image classification, segmentation and object detection and tracking
- Students can apply machine learning and deep learning techniques to solve image-related problems, and deal with practical issues arising in the field (dataset engineering, data augmentation, data normalization)
- Students have seen different examples of image analysis problems and common solution techniques, and are able to acquire additional expert knowledge from the scientific literature and online resources
Categoria modulo
- Introduction
- Basic image processing methods applied to document processing: binarization; segmentation of text into lines, words and characters; connected component analysis.
- Image classification
- applications to OCR: handcrafted features; convolutional neural networks.
- Image classification with small datasets: data augmentation techniques; one-shot learning; transfer learning and pre-trained models.
- Segmentation
- applications to medical images (2D, 3D)
- fully convolutional networks for semantic segmentation.
- Object detection
- face detection with cascading classifiers
- pedestrian detection for autonomous driving
- object tracking in videos.
- Generative models and Image Synthesis
- Applications to Image Inpainting;
- Generative Adversarial Networks;
- Neural style transfer.
Metodologie di insegnamento e apprendimento
Classroom teaching; programming exercises using python and frameworks in python
Bibliografia
- Computer Vision: Algorithms and Applications, Richard Szeliski, 2010
- Deep Learning with Python, Francois Chollet, early 2018, Sections 5, 8.3, 8.5
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