Course - Deep Learning - IT3030
IT3030 - Deep Learning
About
Examination arrangement
Examination arrangement: Oral
Grade: Letter grades
Term:
Autumn
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Oral | 100/100 |
Examination arrangement
Examination arrangement: School exam
Grade: Letter grades
Term:
Spring
| 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
If there is a re-sit examination, the examination form may be changed from written to oral.
Specific conditions
Admission to a programme of study is required:
Computer Science (MIDT)
Computer Science (MTDT)
Industrial Economics and Technology Management (MTIØT)
Informatics (MSIT)
Recommended previous knowledge
Some experience in Python programming
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.
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2024
Language of instruction: English
Location: Trondheim
- Computer Science
- Computer Systems
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Oral
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD Oral 100/100 2023-11-24
-
Room Building Number of candidates
Examination arrangement: School exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD School exam 100/100 D 2024-05-29 15:00 INSPERA
-
Room Building Number of candidates SL310 blå sone Sluppenvegen 14 16 SL310 lilla sone Sluppenvegen 14 36
- * 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"