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: 2019/20
Decided by: PLED F/Pi
Date of Decision: 2019-03-26

General Information

Elective for: BME4-sbh, C4, D4-bg, D4-mai, E4-ss, F4, F4-tf, F4-bg, F4-mai, I4, N4, Pi4-ssr, Pi4-bam
Language of instruction: The course will be given in English on demand

Aim

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

Contents

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.

Admission

Admission requirements:

The number of participants is limited to: 100
Selection: Completed university credits within the programme. Priority is given to students enrolled on programmes that include the course in their curriculum.
The course overlaps following course/s: FYTN14

Reading list

Contact and other information

Course coordinator: Mattias Ohlsson, mattias@thep.lu.se
Course homepage: http://cbbp.thep.lu.se/~mattias/teaching/fytn14/