Valid from: Autumn 2018
Decided by: Professor Thomas Johansson
Date of establishment: 2018-10-15
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
* 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
Types of instruction: Lectures, laboratory exercises
Examination format: Written exam.
The written examination can be replaced by an oral exam given mutual agreement
Grading scale: Failed, pass
Examiner: Associate senior lecturer Maj Stenmark
Assumed prior knowledge: EDAA01 Programming - Second Course or similar
Start date: 2024-11-04
End date: 2025-01-31
Course pace: Full time
Contact the 1st course responsible teacher (Maj Stenmark, via e-mail) AT THE LATEST on September 30 to make sure to take part in the selection process according to the following priorities: 1. PhD students with the CS department, 2. other LTH departments, 3. other LU departments, 4. external, 5. other participants (non-PhD student). The number of places on PhD education level is determined based on the number of students from undergraduate (MSc) level. The supervisor needs to agree for the PhD student to join the course.
Course coordinators: