Topic outline

  • General

    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 project in the medical domain.
    • has the skills to select and utilize 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.

    Content:

    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.

    Lectures on tuesdays (the first one will be on thursday 07.09.2023) at 14:15 - 16:00

    Link to join online: Zoom link

    • 07.09.23 (lecture 1; Care pathways, ethics and place for AI in medicine), Hall: U142/U4 (Otakaari 1/Undergraduate center) 
    • 12.09.23 (lecture 2; Regression and basics of inference), Hall: U141/U3 (Otakaari 1/Undergraduate center)
    • 19.09.23 (lecture 3; Risk stratification via classification), Online lecture
    • 26.09.23 (lecture 4; Model selection and metrics), Online lecture 
    • 03.10.23 (lecture 5; Statistical hypothesis testing), Online lecture 
    • 10.10.23 (lecture 6; Convolutional neural networks: classification), Online lecture
    • 24.10.23 (lecture 7; Convolutional neural networks: structured prediction), Hall: M232/M1 (Otakaari 1/Undergraduate center)
    • 31.10.23 (lecture 8; Bells and whistles of Deep Learning), Hall: M232/M1 (Otakaari 1/Undergraduate center)
    • 07.11.23 (lecture 9; Transformers), Online lecture 
    • 14.11.23 (lecture 10; Reinforcement Learning), Hall: M232/M1 (Otakaari 1/Undergraduate center)
    • 21.11.23 (lecture 11; Uncertainty I), Hall: M232/M1 (Otakaari 1/Undergraduate center)
    • 28.11.23 (lecture 12; Uncertainty II), Hall: M232/M1 (Otakaari 1/Undergraduate center)

    Q&A sessions

    Q&A sessions on thursdays (the first one will be on 14.09.2023) at 14:15 - 16:00 
    Zoom link for Q&A sessions: https://aalto.zoom.us/j/68336195391

    You can ask questions regarding the assignments in Zulip (Zulip link)

    Return the assignments to MyCourses.

    • 14.09.2023 (Session 1)
    • 21.09.2023 (Session 2)
    • 28.09.2023 (Session 3)
    • 05.10.2023 (Session 4)
    • 12.10.2023 (Session 5)
    • 26.10.2023 (Session 6)
    • 02.11.2023 (Session 7)
    • 09.11.2023 (Session 8)
    • 16.11.2023 (Session 9)
    • 23.11.2023 (Session 10)
    • 30.11.2023 (Session 11)

    Important deadlines

    • 29.09.23 (Assignment 1)
    • 27.10.23 (Assignment 2)
    • 17.11.23 (Assignment 3)
    • 17.11.23 (Project topic proposal) 
    • 08.12.23 (Assignment 4)
    • 27.12.23 (Assignment 5)
    • 05.01.24 (Project submission deadline)

    Grading:

    • Lecture attendance (5 %)
    • Five rounds of math and programming assignments (50 %)
    • Course project (45 %)