Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


After the course, the student

  • can explain the fundamentals of neural networks function.
  • is able to construct, design, implement and run a deep learning based AI analysis project in medical domain.
  • has the skills to select and utilize and the tools for applying AI for the different use cases needed for medical applications.
  • is able to identify and develop the data sets that provide the best results.

Credits: 5

Schedule: 05.09.2022 - 05.12.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Leo Kärkkäinen, Simo Särkkä

Contact information for the course (applies in this implementation):

Course Slack channel: TBA

Zoom link for the lectures:

Zoom link for the exercises:

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English


  • valid for whole curriculum period:

    The course will contain the basics in deep learning, introduce the student how machine learning is used in different medical domains, and what technologies are appropriate in different use cases. The work has mathematical and programming exercises,  and a project for designing and teaching a neural network for a medical use case.


  • applies in this implementation

    Lectures Mondays  at 8.15 on Zoom and Exercises on Thursday at 14.15.

    • Lecture 1: What is currently possible with AI?
    • Lecture 2: Introduction to probability. Basics and what is needed for the course.
    • Lecture 3: Machine learning. Unsupervised, self-supervised, supervised, and reinforcement learning.
    • Lecture 4: Neural networks and medical data, Training of networks back-propagation, stochastic gradient descent.
    • Lecture 5: Models of data with spatial and temporal symmetries. Convolutional neural networks (CNNs). Medical imaging, object classification, data augmentation, recurrent neural networks. Physiological signals, like ECG and BCG. Modeling the cardiovascular system.
    • Lecture 6: Autoencoders, predicting the future, and de-noising signals. Generative adversarial networks (GANs)
    • Lecture 7: Learning with less data. Transfer learning, synthetic data. Legislation and ethics in the use of AI.
    • Lecture 8: Natural Language Processing. Attention models. GPT-3. Generating text
    • Lecture 9: AI in embedded systems. Compressing deep neural networks. HW for AI.
    • Lecture 10: Challenges in using AI in health. Adversarial examples. Differential privacy
    • Lecture 11: What could we expect of the future?
    • Lectures 12 - 13 and Exercise 13: Project work reports
    • Lecture 14: Exam

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Project work, exercises, and final examination

  • applies in this implementation

    The course will be graded by Exercises (20%), Exam (40%), and Project work (40%).
    If you wish to do the extended Project work (graded as additional 5cr course), the course will be graded by Exercises (34%) and Exam (66%).

  • valid for whole curriculum period:

    48 h contact teaching, 85 h independent studies


Substitutes for Courses
SDG: Sustainable Development Goals

    3 Good Health and Well-being


Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Autumn I - II
    2023-2024 Autumn I - II