TDIS22 - Deep Learning

Course Code:	TDIS22
Confirmed:	Aug 20, 2025
Valid From:	Jan 18, 2027

Education Cycle:	Second-cycle level
Disciplinary domain:	Technology
Subject group:	Computer Technology
Specialised in:	A1F Second cycle, has second-cycle course/s as entry requirements
Main field of study:	Computer Science

Intended Learning Outcomes (ILO)

On completion of the course the student shall:

Knowledge and understanding

display knowledge of state-of-the-art deep learning algorithms
display knowledge of the purpose of the different components of a deep learning system
display knowledge of how the discussed deep learning techniques work and how they relate to one another

Skills and abilities

demonstrate the ability to design and implement deep learning based applications to address real-world problems
demonstrate the ability to analyse and evaluate the performance of deep learning based solutions experimentally
demonstrate the ability to present and discuss chosen solutions orally as well as in writing

Judgement and approach

demonstrate the ability to select an appropriate deep learning technique and configuration for a given real-world problem
demonstrate the ability to reflect on the impact of deep learning solutions on society with respect to ethics and fairness

Content

This is an introductory course in Deep Learning. The course covers basic and state-of-the-art algorithms for training various deep neural network architectures, alternating theory with practice. The course includes assignments where the students implement various deep learning algorithms. After completing the course, the student shall have acquired a thorough theoretical understanding of, and practical experience with, modern algorithms for deep learning, applied on common deep learning tasks. Specifically, the student should understand and be able to apply all theoretical concepts covered.

The course includes the following elements:

Methodology for training Neural Networks
Neural Network Architectures: Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Graph Convolutional Neural Networks
Learning from no or little data: Unsupervised, Weakly-supervised, Self-supervised learning, as well as Few and Zero-shot learning
Deep Generative Models: Generative Adversarial Networks and Variational Autoencoders
Deep Reinforcement Learning
Explainable Deep Learning Models
Computer Vision Applications: Object Detection, Semantic Segmentation, Image Captioning, Visual Question Answering
Evaluation of Deep Learning Models

Type of instruction

This course consists of lectures, assignments, one project, project tutoring and two project seminars.

Language of instruction is in English.

Entry requirements

Passed courses at least 90 credits within the major subject Computer Engineering, Computer Science, Informatics, Information Systems, Information Technology or Electrical Engineering (with relevant courses in Computer Engineering), or equivalent, or passed courses at least 150 credits from the programme Computer Science and Engineering, and completed courses Artificial Intelligence, 7,5 credits or Python Programming for AI, 7,5 credits, Mathematics for Intelligent Systems, 7,5 credits, and Machine Learning, 7.5 credits or equivalent. Proof of English proficiency is required.

Examination and grades

The course is graded 5, 4, 3 or U.

The credits for the project are awarded for the project report, as well as for two presentations and active participation in the two presentation seminars – one for presenting the project design and one for presenting the final project. The final grade for the course will be determined by the presentations and the project report. The grade for this report is given based on the collaborative part of the report, as well as on the individual part.

Registration of examination:

Name of the Test	Value	Grading
Project ¹	5.5 credits	5/4/3/U
Assignment 1	1 credit	G/U
Assignment 2	1 credit	G/U

¹Determines the final grade of the course, which is issued only when all course units have been passed.

Course literature

Please note that changes may be made to the reading list up until eight weeks before the start of the course.

Principal texts:
Title: Deep Learning (Adaptive Computation and Machine Learning Series), 1st ed., 2016.
Authors: Goodfellow, I., Bengio, Y. and Courville, A.
Publisher: MIT Press
ISBN: 978-0262035613
WEB: www.deeplearningbook.org

A compendium of scientific and popular science papers and articles.

Reference literature:
Title: Deep Learning with Python, 2nd Edition, 2021
Author: Chollet, F.
Publisher: Manning Publications
ISBN: 9781617296864

COURSE SYLLABUS