Advanced Topics in Machine Learning

Instructors Dr. Pavel Laskov, Dr. Blaine Nelson
Class meetings Tue, 14:00 s.t. - 16:00
Location Sand, kleiner Hörsaal, F122
Credit points, Diplom 2 SWS (lectures) + 1 SWS exercizes
Credit points, Master 4 LP (lectures + exercizes)
Office hours by appointment
Instruction language English
Examination area Practical informatics


  • New: There will be no class on 26.06: both of us are attending ICML. Check the updated lecture schedule below.
  • Exercise meeting time changed from c.t. to s.t.; first meeting will be held on Wed, 02.05
  • Course start time changed from c.t. to s.t.

Course description:

The course offers an overview of advanced machine learning methods and applications. We will focus on so-called "kernel methods", in which the learning problems are formulated via paiwise relationships between data points. After an introduction of the underlying mathematical properties of kernel functions, various algorithms and practical applications of kernel methods will be considered. The course requires a basic knowledge of machine learning methods and the underlying mathematical and statistical instruments.

Examination and grades:

Diploma students can request an examination on this course in a usual manner and in any reasonable combination with another course. The amount of work covered by this course constitute 2 SWS for lectures and 1 SWS for graded exercises. An exercise certificate will be issued at the end of the semester. The grade for master students will be composed of the results of the written final exam (70%) and exercise grades (30%).


John Shawe-Taylor and Nello Cristianini: Kernel Methods for Pattern Analysis . Cambridge University Press, 2004.


Date Location Topic Instructor Slides
Tue, 17.04 F122 Course Introduction, application examples Nelson Lecture 1
Tue, 24.04 F122 Overview of kernel methods Laskov Lecture 2
Tue, 08.05 F122 Kernel functions: mathematical foundations Nelson Lecture 3
Tue, 15.05 F122 Elementary kernel algorithms Laskov Lecture 4
Tue, 22.05 F122 Eigendecompositions, kernel PCA and CCA Nelson Lecture 5
Tue, 05.06 F122 Empirical risk minimization, Support Vector Machines Nelson Lecture 6
Tue, 12.06 F122 "Big" learning Laskov Lecture 7
Tue, 19.06 F122 Online and incremental learning Laskov Lecture 8
Tue, 26.06 F122 NO CLASS
Tue, 03.07 F122 Kernel methods for structured inputs Laskov Lecture 9
Tue, 10.07 F122 Kernel methods for structured outputs Laskov Lecture 10
Tue, 17.07 F122 Learning in adversarial environments Nelson Lecture 11
Tue, 24.07 F122 Classifier evasion methods, adversarial games Nelson Lecture 12
Tue, 31.07 F122 Final exam

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