DEEP LEARNING
Stampa
Enrollment year
2019/2020
Academic year
2020/2021
Regulations
DM270
Academic discipline
ING-INF/05 (DATA PROCESSING SYSTEMS)
Department
DEPARTMENT OF ELECTRICAL,COMPUTER AND BIOMEDICAL ENGINEERING
Course
COMPUTER ENGINEERING
Curriculum
Embedded and Control Systems
Year of study
Period
2nd semester (08/03/2021 - 14/06/2021)
ECTS
6
Lesson hours
45 lesson hours
Language
English
Activity type
ORAL TEST
Teacher
PIASTRA MARCO (titolare) - 6 ECTS
Prerequisites
Foundations of linear algebra and multivariable calculus. Practical experience with at least one programming language. Some acquaintance with Python and Numpy.
Learning outcomes
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.
Course contents
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
Teaching methods
Lectures (hours/year in lecture theatre): 30
Practical class (hours/year in lecture theatre): 16
Practicals / Workshops (hours/year in lecture theatre): 0
Reccomended or required readings
See the home page of the course (http://vision.unipv.it/DL) for lecture slides, suggested readings and software for the exercises.
Assessment methods
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.
Further information
Sustainable development goals - Agenda 2030