DEEP LEARNING
Stampa
Anno immatricolazione
2020/2021
Anno offerta
2021/2022
Normativa
DM270
SSD
ING-INF/05 (SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI)
Dipartimento
DIPARTIMENTO DI INGEGNERIA INDUSTRIALE E DELL'INFORMAZIONE
Corso di studio
COMPUTER ENGINEERING
Curriculum
Data Science
Anno di corso
Periodo didattico
Secondo Semestre (07/03/2022 - 17/06/2022)
Crediti
6
Ore
45 ore di attività frontale
Lingua insegnamento
English
Tipo esame
ORALE
Docente
PIASTRA MARCO (titolare) - 6 CFU
Prerequisiti
Foundations of linear algebra and multivariable calculus. Practical experience with at least one programming language. Some acquaintance with Python and Numpy.
Obiettivi formativi
The course follows a conceptual pathway that starting from simple linear regression to the sophisticated aspects of state-of-art of deep convolutional neural networks, deep recurrent networks and deep reinforcement learning. A unifying mathematical approach is followed throughout this path, to encompass and make it possible to understand the basic features of modern software frameworks for deep learning, such as TensorFlow.
Programma e contenuti
1) Deep Supervised Learning

Algebraic model, foundations of tensor calculus
Learning as representation, evaluation and optimization
Single-layer networks as universal approximators
Dataset in tensor representation for calculus
Flow diagrams, automatic differentiation
Regression and classification, softmax
Deep layered representation, modularity
Ottimizzazione

2) Deep Convolutional Neural Networks

Convolutional layers and complex architectures
Data augmentation and Transfer learning
Layered learning, different optimization processses
Fallibility and adversarial models
Classification, object detection, segmentation

3) Deep Recurrent Networks

Temporal unfolding, shared-parameters layers
Long-Short Term Memory (LSTM)

4) Deep Reinforcement Learning

On-policy and off-policy learning
Actor critic and advance function
Neural MCTS: AlphaZero e MuZero
Metodi didattici
Lectures (hours/year in lecture theatre): 30
Practical class (hours/year in lecture theatre): 16
Practicals / Workshops (hours/year in lecture theatre): 0
Testi di riferimento
See the home page of the course (http://vision.unipv.it/DL) for lecture slides, suggested readings and software for the exercises.
Modalità verifica apprendimento
The evaluation includes the realization of a project, to be agreed in advance. The final exam is an interview about the theory, together with the discussion of the project.
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