course-details-portlet

IT3030

Deep Learning

Credits 7.5
Level Second degree level
Course start Spring 2021
Duration 1 semester
Language of instruction English
Location Trondheim
Examination arrangement Portfolio assessment

About

About the course

Course content

The course is a follow-up to TDT4173 Machine Learning. It gives thorough coverage of deep learning. The course covers both mathematical and computational foundation for deep learning, practical applications such as processing of images, text, and other modalities. In addition, deep reinforcement learning will be discussed. Modern software frameworks for deep learning will be introduced.

Learning outcome

Knowledge:
General principles for learning/adaptive systems
Mathematical and computational foundation for deep learning
How to use deep learning in diverse practical applications

Skills:
Analyze different frameworks for deep learning in specific application domains
Ability to analyze the mathematical foundation for diverse deep learning published in the literature
Build computational systems that achieve deep learning

General competences:
Understand deep learning's basis in mathematics and cognitive science
Understand possibilities and limitations of deep learning in practical settings

Learning methods and activities

Lectures, self study

Further on evaluation

Grade is based on portfolio assessment. All projects must be done individually and must be completed to pass the course. Results for each part are given as percentages, while the final grade is in pass/fail. Note that there is no re-sit exam in this course. Repetition of assessment is only possible in the teaching semester.

Specific conditions

Admission to a programme of study is required:
Computer Science (MIDT)
Computer Science (MTDT)
Industrial Economics and Technology Management (MTIØT)
Informatics (MIT)
Informatics (MSIT)

Required previous knowledge

TMA4115 Mathematics 3, TDT4120 Algorithms and data-structures, TDT4171 Methods in Artificial Intelligence, and TDT4173 Machine learning.

Course materials

Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning (2016). MIT Press.
Supplementary articles will be handed out as needed.

Subject areas

  • Computer Science
  • Computer Systems

Contact information

Lecturers

Department with academic responsibility

Department of Computer Science

Examination

Examination

Examination arrangement: Portfolio assessment
Grade: Passed/Failed

Ordinary examination - Spring 2021

Arbeider 1
Weighting 25/100
Arbeider 1
Weighting 25/100
Arbeider 1
Weighting 25/100
Arbeider 1
Weighting 25/100