227-0432-00L  Learning, Classification and Compression

SemesterSpring Semester 2021
LecturersE. Riegler
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
227-0432-00 VLearning, Classification and Compression2 hrs
Thu09:15-11:00IFW A 32.1 »
E. Riegler
227-0432-00 ULearning, Classification and Compression1 hrs
Thu11:15-12:00IFW A 32.1 »
E. Riegler

Catalogue data

AbstractThe focus of the course is aligned to a theoretical approach of learning theory and classification and an introduction to lossy and lossless compression for general sets and measures. We will mainly focus on a probabilistic approach, where an underlying distribution must be learned/compressed. The concepts acquired in the course are of broad and general interest in data sciences.
ObjectiveAfter attending this lecture and participating in the exercise sessions, students will have acquired a working knowledge of learning theory, classification, and compression.
Content1. Learning Theory
(a) Framework of Learning
(b) Hypothesis Spaces and Target Functions
(c) Reproducing Kernel Hilbert Spaces
(d) Bias-Variance Tradeoff
(e) Estimation of Sample and Approximation Error

2. Classification
(a) Binary Classifier
(b) Support Vector Machines (separable case)
(c) Support Vector Machines (nonseparable case)
(d) Kernel Trick

3. Lossy and Lossless Compression
(a) Basics of Compression
(b) Compressed Sensing for General Sets and Measures
(c) Quantization and Rate Distortion Theory for General Sets and Measures
Lecture notesDetailed lecture notes will be provided.
Prerequisites / NoticeThis course is aimed at students with a solid background in measure theory and linear algebra and basic knowledge in functional analysis.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersE. Riegler
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkhttps://www.mins.ee.ethz.ch/teaching/LCC/
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
Data Science MasterCore ElectivesWInformation
Doctoral Dep. of Information Technology and Electrical EngineeringDoctoral and Post-Doctoral CoursesWInformation
Electrical Engineering and Information Technology MasterSpecialization CoursesWInformation
Electrical Engineering and Information Technology MasterRecommended SubjectsWInformation
Mathematics BachelorSelection: Further RealmsWInformation
Mathematics MasterSelection: Further RealmsWInformation
Physics MasterGeneral ElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation
Statistics MasterSubject Specific ElectivesWInformation