Course: IHB0090 Artificial Intelligence in Healthcare

General information

Collapse all Expand all

Introduction to the course Page
Course info (link to Study Information System) URL
Action plan File

Darker pink - Labs/contact lecture
Lighter pink - Independent work (might consists an assignment which needs to be submitted)
Bold: Matlab assignments
Course feedback
General announcements Forum
Coordinator - Maie Bachmann - firstname.lastname@taltech.ee - please include IHB0090 in subject line
NB! Contact lectures-labs take place in room SOC-407
Exams:
21.05.2025 11:00
27.05.2025 11:00
04.06.2025 11:00
Multiple-choice exam and you have to be personally present.
REGISTER in OIS (Study information system) - currently not open
As this course follows the session based study style the contact lectures-labs do not take place every week. Contact lectures-labs are indicated in OIS timetable with the room number. The Action Plan has date and TIME presented for contact lectures, the others are non-contact weeks, which usually consist of a video lecture + additional independent work in Moodle. If not otherwise specified, the uploaded material (non-contact week) has to be processed during that week.
MATLAB self-paced courses freely available for TalTech students:
If you have previous programming experience: MATLAB Onramp (2 hours), short introductory tutorial https://www.mathworks.com/learn/tutorials/matlab-onramp.html
If you have no previous programming experience or just a slight experience: MATLAB Fundamentals is suggested (takes about 2-3 full days) https://www.mathworks.com/learn/online-courses/matlab-fundamentals.html
Use your TalTech e-mail address to access the MATLAB campus license.
University account: http://pass.ttu.ee
MathWorks account: https://se.mathworks.com/
Matlab: https://se.mathworks.com/academia/tah-portal/tallinn-university-of-technology-40687166.html#get
For your information:
FDA Releases Artificial Intelligence/Machine Learning Action Plan (January 12, 2021)
https://www.fda.gov/news-events/press-announcements/fda-releases-artificial-intelligencemachine-learning-action-plan

European AI Act text web page
Additional Reading: 2025.02.04 How is AI shaping the future of healthcare in Finland and Estonia? Folder

https://www.tehnopol.ee/en/how-ai-is-shaping-the-future-of-healthcare-in-finland-and-estonia/

Not available unless: You belong to Registered2025

Introduction and Assessment Principles

Lecture Intro & Assessment File

PDF document

AI Motivation and Definitions,

Prof Ivo Fridolin: firstname.lastname@taltech.ee
Motivation & Definitions AI Healthcare File

PDF document

Structure and components of ML

Structure and components of machine learning (slides) File
Structure and components of machine learning (handouts) File
Example of basic linear classifier File
Lecture: Structure and components of ML BigBlueButton
Dr Kristjan Pilt: firstname.lastname@taltech.ee

Naive Bayes Classifier and Feature Discretization

This is introductory video lecture to Naive Bayes Classifier. It is 43 minutes long and contains basics of the probability calculations with the examples. The lecture ends with the definition of Naive Bayes classifier. Who is not confident in the calculation of probability and don't remember exactly the concepts of it then please look this video lecture through before next Wednesday (26th of February).
Introduction to Naive Bayes classifier and feature discretization (slides) File
Video lecture: Introduction to Naive Bayes classifier and feature discretization URL
Naive Bayes Classifier and Feature Discretization (2025) - Lecture BigBlueButton
Naive Bayes Classifier and Feature Discretization (slides) File
Lecture task - Excel file
Files for practical work assignment.
IMPORTANT!!! Datasets are allowed to use only in the purposes of this course!
Naive Bayes classifier task - test group (text file)
Naive Bayes classifier task - test group (Excel file)
Naive Bayes classifier task - training group (text file)
Naive Bayes classifier task - training group (Excel file)
How to create correlation matrix in Excel (video) File

Video file (MP4)

AI Act

Work through the material, no additional assignment (nothing to upload)
Watch the following Coursera content:
Introduction to AI Ethics and Regulatory Compliance
Introduction to the Regulatory Landscape: EU AI Act
Practical Issues in AI and ML Applications in Medicine
Read the concise summary of the AI Act
Additional material: AI Act text web page

"The AI Act entered into force on 1 August 2024, and will be fully applicable 2 years later on 2 August 2026, with some exceptions:
prohibitions and AI literacy obligations entered into application from 2 February 2025
the governance rules and the obligations for general-purpose AI models become applicable on 2 August 2025
the rules for high-risk AI systems - embedded into regulated products - have an extended transition period until 2 August 2027" from https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

Introduction to supervised learning & linear regression

Logistic regression and introduction to artificial neural networks

Classification validation methods

Lecture: Classification validation methods BigBlueButton
Classification Validation Methods File
Lab Assignment (week 1) BigBlueButton
Lab_Assignment_3_and_4 Folder
Ardo Allik: firstname.lastname@gmail.com
Please send lab assignment also to this e-mail!

Multiclass classification

Lecture: Multiclass classification URL

Please let me know if the link/video does not work.
Multiclass Classification File
Lab Assignment (week 2) BigBlueButton
Ardo Allik: firstname.lastname@gmail.com

Decision tree classification

Decision Tree Classification File
Ardo Allik: firstname.lastname@gmail.com

30.04 & 14.05 Introduction to unsupervised learning. Hierarchical clustering. Partitional clustering (K-means).

07.05 HealthCode AI

Not available

_

Not available

_

Not available

_

Not available

_

Not available

_

Not available

Weekly outline

General information

Darker pink - Labs/contact lectureLighter pink - Independent work (might consists an assignment which needs to be submitted)Bold: Matlab assignments

Introduction and Assessment Principles

AI Motivation and Definitions,

Structure and components of ML

Naive Bayes Classifier and Feature Discretization

AI Act

Introduction to supervised learning & linear regression

Logistic regression and introduction to artificial neural networks

Classification validation methods

Multiclass classification

Decision tree classification

30.04 & 14.05 Introduction to unsupervised learning. Hierarchical clustering. Partitional clustering (K-means).

07.05 HealthCode AI

_

_

_

_

_

Additional material

Darker pink - Labs/contact lecture
Lighter pink - Independent work (might consists an assignment which needs to be submitted)
Bold: Matlab assignments