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Introduction to AI and Machine Learning in Image Segmentation

ECTS Credits: 3

Course Parameters:

  • Language: English
  • Level of Course: PhD course
  • Semester/Quarter: Q1 2024
  • Hours per Week: 3 weeks + homework = 80 hours in total.
  • Capacity Limits: 2 participants

Objectives of the Course:

The course aims to provide the PhD students with foundational knowledge and practical skills in using AI and machine learning techniques for image segmentation, with a focus on applications in various fields.

Learning Outcomes and Competences:

Upon completing the course, the student should be able to:

  • Understand the fundamentals of AI and machine learning algorithms.
  • Apply AI and machine learning techniques to image segmentation tasks.
  • Design and implement image segmentation strategies for real-world applications.
  • Evaluate and fine-tune machine learning models for image segmentation.
  • Present and discuss the results of image segmentation projects.

Compulsory Program:

Entire course

Course Contents:


Introduction to AI and Machine Learning

  • Overview of AI and machine learning concepts.
  • The fundamentals of machine learning
    • Types of learning (supervised, unsupervised, semi-supervised)
    • The main challenges of machine learning: Data quality and quantity, model fit.
    • Training models and gradient descent.
    • Model testing, validation, selection, and evaluation.
    • Model hyperparameters and tuning.
  • Introduction to common machine learning models
    • Linear regression, Logistic regression, SVM, CART and Random Forests
  • Dimensionality reduction
    • The curse of dimensionality
    • Principal component analysis (PCA)

Practical Application

  • Worked through examples with Python/Jupyter Notebooks

Week 2:

Fundamentals of Image Processing

  • Introduction to images and image segmentation.
  • Image preprocessing techniques: Noise reduction and image enhancement.
  • Image filters and feature extraction.
  • Neural networks

Supervised Learning for Image Segmentation

  • Training data preparation.
  • Building and training convolutional neural networks (CNNs) for image segmentation.
  • Evaluating model performance.

Practical Application

  • Machine learning models for image processing
  • Implementing a CNN for image segmentation.

Week 3:

Project Work

  • Project work on real-world image segmentation tasks.
  • Fine-tuning models and optimizing segmentation performance.
  • Data presentation and discussions of project outcomes.


 Basic understanding of coding programs such as Python or R

Name of Lecturer:

Mihailo Azhar

Type of Course/Teaching Methods:

The course combines lectures on campus, hands-on exercises (tutorials), and project work. Students will work on practical image segmentation projects.


 Course materials and references will be provided at the beginning of the course.

Participants are expected to have acquired “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e: Concepts, Tools, and Techniques to Build Intelligent Systems” – Aurelien Geron, before course start.

Course homepage:


Course Assessment:

Course participation will be assessed based on:

- Active attendance

- Completion of hands-on exercises (tutorials)

- Successful implementation of image segmentation in a real-life project and presentation of findings


 Aarhus University, Ecoscience AU. Marine Biodiversity and experimental ecology

Special Comments on This Course:

The course will take place at Aarhus University, Ecoscience at Risø, 399 Frederiksborgvej, 4000 Roskilde, Denmark

Course Fee:

PhD students enrolled at a Danish University may not be charged a course fee. for PhD students enrolled at non-Danish university a fee of DKK 3600 will be charged.


Deadline for registration is 30 November 2023. Information regarding admission will be sent out no later than 4 December 2023.

For registration: send an e-mail to mihailo.azhar@ecos.au.dk stating your name and affiliation (University)

If you have any questions, please contact Mihailo or Marc, mihailo.azhar@ecos.au.dk, mca@ecos.au.dk

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