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.
LEARNING OUTCOMES
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
leo.karkkainen@aalto.fi
Teaching assistant: Joel Jaskari
joel.jaskari@aalto.fi
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
CONTENT, ASSESSMENT AND WORKLOAD
Content
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
Exercises:
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
At
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.
Workload
Valid 01.08.2020-31.07.2022:
48 h contact teaching, 85 h independent studies
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
See MyCourses
Prerequisites
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
FURTHER INFORMATION
Details on the schedule
Applies in this implementation:
The course lectures will be held on Mondays from 8.15am to 10amstarting 7th of September and ending 7th of December.
https://aalto.zoom.us/j/61235758006
Meeting
ID: 612 3575 8006Exercises will be held on the same time on Friday.
https://aalto.zoom.us/j/65250762675
Meeting ID: 652 5076 2675