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eXplainable Machine Learning / Wyjaśnialne Uczenie Maszynowe - 2023

eXplainable Machine Learning course for Machine Learning (MSc) studies at the University of Warsaw.

Winter semester 2022/23 @pbiecek @hbaniecki

Design Principles

The course consists of lectures, computer labs and a project.

The design of this course is based on four principles:

  • Mixing experiences during studies is good. It allows you to generate more ideas. Also, in mixed groups, we can improve our communication skills,
  • In eXplainable AI (XAI), the interface/esthetic of the solution is important. Like earlier Human-Computer Interaction (HCI), XAI is on the borderline between technical, domain and cognitive aspects. Therefore, apart from the purely technical descriptions, the results must be grounded in the domain and should be communicated aesthetically and legibly.
  • Communication of results is important. Both in science and business, it is essential to be able to present the results concisely and legibly. In this course, it should translate into the ability to describe one XAI challenge in the form of a short report/article.
  • It is worth doing valuable things. Let's look for new applications for XAI methods discussed on typical predictive problems.

Meetings

Plan for the winter semester 2022/2023. UW classes are on Fridays.

  • 2022-10-07 -- Introduction, slides, audio
  • 2022-10-14 -- Break-Down / SHAP, slides, audio, code examples
  • 2022-10-21 -- LIME / LORE, slides, audio, code examples
  • 2022-10-28 -- CP / PDP, slides, code examples
  • 2022-11-04 -- PROJECT: First checkpoint - Choose a topic and be familiar with the attached materials.
  • 2022-11-18 -- VIP / MCR, slides, audio, code examples
  • 2022-11-25 -- Fairness, slides, audio, code examples
  • 2022-12-02 -- Explanations specific to neural networks, slides & Evaluation of explanations, slides
  • 2022-12-09 -- PROJECT: Second checkpoint - Provide initial experimental results and/or code implementation.
  • 2022-12-16 -- Fairness and project consultations
  • 2022-12-22 -- Project consultations
  • 2023-01-13 -- Student presentations
  • 2023-01-20 -- Student presentations
  • 2023-01-27 -- PROJECT: Final presentation - Present final experimental results and/or code implementation.

How to get a good grade

From different activities, you can get from 0 to 100 points. 51 points are needed to pass this course.

Grades:

  • 51-60: (3) dst
  • 61-70: (3.5) dst+
  • 71-80: (4) db
  • 81-90: (4.5) db+
  • 91-100: (5) bdb

There are four key components:

  • Homeworks (0-24)
  • Presentations (0-10)
  • Project (0-36)
  • Exam (0-30)

Homeworks (24 points)

Presentations (10 points)

Presentations can be prepared by one or two students. Each group should present a single paper related to XAI published in the last 3 years (journal or conference). Each group should choose a different paper. Here are some suggestions:

// More suggestions for computer vision:

Project (36 points)

List of topics

XAI stories ebook (previous editions):

Exam (30 points)

A written exam will consist of simple exercises based on the materials from Lectures and Homeworks.

Literature

We recommend to dive deep into the following books and explore their references on a particular topic of interest: