Course Syllabus for

Applied Machine Learning
Tillämpad Maskininlärning

EDAN95F, 7.5 credits

Valid from: Autumn 2018
Decided by: Professor Thomas Johansson
Date of establishment: 2018-10-15

General Information

Division: Computer Science (LTH)
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course codes: EDAN95, EDAN96
Teaching language: English


To give an introduction to several subdomains of machine learning and to give an orientation about fundamental methods and algorithms within these domains. To convey knowledge about breadth and depth of the domain.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must

Course Contents

* unsupervised and supervised learning, classification and regression * neural networks, including convolutional neural networks, recurrent neural networks and deep learning * bayesian learning * reinforcement learning * support vector machines, decision trees, random forests, ensemble methods * hardware and software architectures for machine learning, parallelisation, use of GPUs

Course Literature

Instruction Details

Types of instruction: Lectures, laboratory exercises

Examination Details

Examination format: Written exam. The written examination can be replaced by an oral exam given mutual agreement
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: EDAA01 Programming - Second Course or similar

Course Occasion Information

Contact and Other Information

Course coordinator: Elin A. Topp <>
Web page:

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