Course syllabus

Introduktion till artificiella neuronnätverk och deep learning
Introduction to Artificial Neural Networks and Deep Learning

EXTQ40, 7,5 credits, A (Second Cycle)

Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED F/Pi
Date of Decision: 2023-04-18

General Information

Main field: Machine Learning, Systems and Control.
Compulsory for: MMSR1
Elective for: BME4-sbh, C4, D4-bg, D4-mai, E4-ss, F4, F4-tf, F4-mai, I4, MFOT1, N4, Pi4-ssr, Pi4-bam
Language of instruction: The course will be given in English on demand


The overall aim of the course is to give students a basic knowledge of artificial neural networks and deep learning, both theoretical knowledge and how to practically use them for typical problems in machine learning and data mining.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must


The course covers the most common models in artificial neural networks with a focus
on the multi-layer perceptron. The course also provides an introduction to deep
l earning. Selected topics:

Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: The examination consists of a written reports on the mandatory computer exercises and an oral or written test at the end of the course.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Code: 0122. Name: Test.
Credits: 6. Grading scale: TH. Assessment: Written or oral examination.
Code: 0222. Name: Computer Exercises.
Credits: 1,5. Grading scale: UG. Assessment: Approved computer exercises.


Admission requirements:

The number of participants is limited to: 250
Selection: Completed university credits within the program. Priority is given to students enrolled on programmes that include the course in their curriculum. Among these students priority is given to those in the master's programme in Machine Learning, Systems and Control, for whom the course is compulsory.
The course overlaps following course/s: FYTN14

Reading list

Contact and other information

Teacher: Mattias Ohlsson,
Course coordinator: Patrik Edén,
Course homepage:
Further information: The course is given by the Faculty of Science and does not follow the study period structure. The course will partly be held online.