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: 07.09.2020 - 14.12.2020

Teacher in charge (valid 01.08.2020-31.07.2022): Leo Kärkkäinen, Simo Särkkä

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

Contact information for the course (valid 17.08.2020-21.12.2112):

Lecturer: Leo Kärkkäinen

Teaching assistant: Joel Jaskari

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English


  • Valid 01.08.2020-31.07.2022:

    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 and Exercises on Fridays 8-10 am. The. venue depends on the Covid-19 situation: Remote or TU6 (1199), TUAS, Maarintie 8, Exercises Fridays.

    • Lecture 1: What is currently possible with AI? Selection of project work topics.
    • 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 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 01.08.2020-31.07.2022:

    Project work, exercises, and final examination

  • Applies in this implementation:

    Project work, exercises, and final examination

    Each week (except for 1st week) we will have some
    derivations and questions related to the course content, and programming
    tasks. At the exercise you have to mark the ones you have solved and be
    prepared to present these.

    At minimum finish 50% of the exercises. 
    20% of final score is based on amount of exercises completed in an acceptable level.

    Project work
    the beginning of the course some project work topics will be provided,
    but you can also suggest your own according to your personal preference.
    You can work on this alone or in pairs.

    At the end of the course, you will give a lecture and demonstration of your work at the lecture time.

    Project work is 40% score

    Final exam
    40% of the score.

    Notice: By doing a more extensive project work one can earn extra credits.

  • Valid 01.08.2020-31.07.2022:

    48 h contact teaching, 85 h independent studies


Study Material
  • Valid 01.08.2020-31.07.2022:

    See MyCourses

  • Valid 01.08.2020-31.07.2022:

    Python progamming and linear algebra. Mathematica and Matlab skills will be useful

SDG: Sustainable Development Goals

    3 Good Health and Well-being


Details on the schedule