263-5052-00L  Interactive Machine Learning: Visualization & Explainability

SemesterSpring Semester 2024
LecturersM. El-Assady
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
263-5052-00 GInteractive Machine Learning: Visualization & Explainability3 hrs
Thu11:15-14:00CAB G 61 »
M. El-Assady
263-5052-00 AInteractive Machine Learning: Visualization & Explainability1 hrsM. El-Assady

Catalogue data

AbstractVisual Analytics supports the design of human-in-the-loop interfaces that enable human-machine collaboration. In this course, will go through the fundamentals of designing interactive visualizations, later applying them to explain and interact with machine leaning models.
ObjectiveThe goal of the course is to introduce techniques for interactive information visualization and to apply these on understanding, diagnosing, and refining machine learning models.
ContentInteractive, mixed-initiative machine learning promises to combine the efficiency of automation with the effectiveness of humans for a collaborative decision-making and problem-solving process. This can be facilitated through co-adaptive visual interfaces.

This course will first introduce the foundations of information visualization design based on data charecteristics, e.g., high-dimensional, geo-spatial, relational, temporal, and textual data.

Second, we will discuss interaction techniques and explanation strategies to enable explainable machine learning with the tasks of understanding, diagnosing, and refining machine learning models.

Tentative list of topics:
1. Visualization and Perception
2. Interaction and Explanation
3. Systems Overview
Lecture notesCourse material will be provided in form of slides.
LiteratureWill be provided during the course.
Prerequisites / NoticeBasic understanding of machine learning as taught at the Bachelor's level.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersM. El-Assady
Typeend-of-semester examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationFinal grade: 50% written exam, 50% mandatory project work
Written aidsNone

Learning materials

 
Main linkInformation
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places190 at the most
PriorityRegistration for the course unit is until 03.03.2024 only possible for the primary target group
Primary target groupRobotics, Systems and Control MSc (159000)
Electrical Engin. + Information Technology MSc (237000)
Doctorate Inform. Tech. & Electrical Engineering (239002)
Cyber Security MSc (260000)
Cyber Security MSc (EPFL) (260100)
Data Science MSc (261000)
Computer Science MSc (263000)
Doctorate Computer Science (264002)
Statistics MSc (436000)
Waiting listuntil 10.03.2024

Offered in

ProgrammeSectionType
CAS in Computer ScienceFocus Courses and ElectivesWInformation
Cyber Security MasterElective CoursesWInformation
Cyber Security MasterElective CoursesWInformation
Data Science MasterCore ElectivesWInformation
Computer Science MasterMinor in Computer VisionWInformation
Computer Science MasterElective CoursesWInformation
Computer Science MasterElective CoursesWInformation
Computer Science MasterMinor in Machine LearningWInformation
Statistics MasterSubject Specific ElectivesWInformation