Advanced Topics in Learning-based Computer Vision
Maskininlärning för datorseende avancerad kurs

FMA325F, 7.5 credits

Valid from: Autumn 2024
Decided by: Maria Sandsten
Date of establishment: 2024-05-07

General Information

Division: Mathematics
Course type: Third-cycle course
Teaching language: English

Aim

This course will cover advanced topics in computer vision with a focus on machine learning,

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• Understand advanced concepts and techniques in machine learning that are used in computer vision.
• Understand and discuss the current state-of-the-art and future directions in machine learning-based methods for computer vision.
• Be familiar with common network architectures, losses and datasets used in the field.

Competences and Skills

For a passing grade the doctoral student must

• Be able to apply common machine learning techniques used in computer vision.
• Be able to analyse and critique recent research papers in the field.
• Be able to apply and understand common evaluation methodologies and benchmarking approaches.

Course Contents

The course is focused on machine learning methods and paradigms that are used in modern computer vision.

Course Literature

Scientific articles that highlight both the history and current research frontier within the field.

Instruction Details

Types of instruction: Seminars, self-study literature review

Examination Details

Examination format: Seminars given by participants
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: The course participants are assumed to have research experience in a topic relevant for the course.
Selection criteria: The course is limited to 12 participants. Preference is given to students admitted to the research subjects Mathematics (TEFMAF00), Numerical Analysis (TEEDAFNA) och Mathematical Statistics (TEFMSF00).

Course Occasion Information

Start date: 2024-11-04. Start date is approximate.
End date: 2025-01-19
Course pace: Full time

Application Information

The Course application is sent to jessica.kareseit@math.lth.se

Contact and Other Information

Course coordinators:

• Viktor Larsson <viktor.larsson@math.lth.se>
• Magnus Oskarsson <magnus.oskarsson@math.lth.se>