IT3030 - Deep Learning


Examination arrangement

Examination arrangement: School exam
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
School exam 100/100 4 hours D

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. Modern software frameworks for deep learning will be introduced and used for some projects, while other projects will require relatively low-level coding in Python or similar languages.

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. Assignments will be published during the semester, from which a subset must be solved successfully to be accepted to be accepted for the exam.

Compulsory assignments

  • Compulsory assignments

Further on evaluation

Note that there is no re-sit exam in this course.

Specific conditions

Compulsory activities from previous semester may be approved by the department.

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.

More on the course



Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2023

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Computer Science
  • Computer Systems
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science


Examination arrangement: School exam

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Spring ORD School exam 100/100 D INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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