logo-polimi
Loading...
Degree programme
Programme Structure
Show/Search Programme
Course Details
Save Document
Degree Programme
Read Degree Programme
Faculty
Infrastructures
Quantitative data
International context
Customized Schedule
Your customized time schedule has been disabled
Enable
Search
Search a Professor
Search a Course
Search a Course (system prior D.M. n. 509)
Search Lessons taught in English

Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Educational activities
BIdentifying activities
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2019/2020
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
Track T2A - COMPUTER SCIENCE AND ENGINEERING
Programme Year 2

Course Details
ID Code 054307
Course Title ARTIFICIAL NEURAL NETWORKS AND DEEP LEARNING
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester First Semester
Course Description Neural networks are mature, flexible, and powerful non-linear data-driven models that have successfully solved many complex tasks in science and engineering. The advent of the deep learning paradigm, i.e., training (neural) networks to simultaneously learn an optimal data representation and a model solving the requested task, has further boosted neural networks research and development. These models nowadays achieve human-like performance in natural language processing, text modeling, gene expression modeling, and image recognition, to name a few examples. This course provides a broad introduction to artificial neural networks (ANN), starting from the traditional feedforward (FFNN) and recurrent (RNN) neural networks architectures, till the most successful deep-learning models including convolutional neural networks (CNN) and long short-term memories (LSTM). The course aims at providing students with a theoretical background and the practical skills to understand and use ANN and, at the same time, become familiar and with Deep Learning for solving complex engineering problems.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
B
ING-INF/05
INFORMATION PROCESSING SYSTEMS
5.0

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZMatteucci Matteo
manifesti v. 3.1.9 / 3.1.9
Area Servizi ICT
19/11/2019