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Machine Learning I

The module "Machine Learning 1" is composed of the integreated lecture (6 LP) and one optional compulsory course (3 LP).

Teaching coordination and course inscription are done via the ISIS site of the integrated lecture. Detailed information on course formalities and accreditability are also summarized there. A description for external participants (Nebenhörer and Gasthörer) can be found below in the section on the online part of the course.

As thematical preparation, it is recommended to visit the MATLAB course or the mathematical foundations course which are also accreditable as optional compulsory course part and which (Cave!) take place in the weeks prior to the start of the lecture period.

Integrated lecture
Thursday, 10:15 am - 11:45 am (starting 25.10.2012)
room: MAR 0.016
Exercise session
Thursday, 12:15 pm - 13:45 pm (starting 25.10.2012)
room: MAR 0.016
Responsible
Prof. Dr. Klaus-Robert Müller
office hours: by appointment
Person of contact
Dr. Franz Király
office hours: by appointment

Topics

In the lecture, introductory topics in the field of machine learning are presented.

After the lecture, the learnt methods are revisited and last week's exercises are explained in the exercise session.

Both lecture and exercise session are usually held in English.

The scheduled topics are:

List of Lecture and Exercise Topics
25.10.2012
Introduction to Machine Learning and Statistics
01.11.2012
Bayes Decision Theory
08.11.2012
Maximum Likelihood Estimation and Bayes Learning
15.11.2012
Principal Component Analysis
22.11.2012
Independent Component Analysis
29.11.2012
k-means Clustering
06.12.2012
Expectation Maximization
13.12.2012
k-nearest Neighbor
20.12.2012
Fisher Discriminant Analysis
10.01.2013
Learning Theory and Kernel Methods
17.01.2013
Support Vector Machines
24.01.2013
Kernel Ridge Regression and Gaussian Processes
31.01.2013
Model Selection
07.02.2013
Neural Networks
14.02.2013
Overview and Outlook

Changes to the schedule will be announced through the ISIS mailing list.

Lecture slides and course materials are available at the ISIS on-line course.

Prerequisites

The following are optional prerequisites which are helpful but not necessary for taking the course:

  • Basic knowledge in linear algebra and calculus, as presented in the respective modules (German: Lineare Algebra, Analysis)
  • Basic knowledge in probability theory, as presentid in the module stochastics (German: Elementare Stochastik)
  • Basic programming knowledge, programming in MATLAB

The necessary prerequisites are in general presented and explained in the lecture or the exercise session.

For a brief review of the technical prerequisites, it is recommended to take the preparatory MATLAB and math foundation courses.

Accreditation

A successful participation in the integrated lecture and at least one optional compulsory course are mandatory for the registration for the machine learening 1 module exam.

The topics of the module exam are composed of the lecture topics and possibly topics treated in the optional compulsory exam. The module exam is a written exam which takes place at the end of the term and whose length is usually two hours. The first re-exam is a written exam, the second re-exam is an oral exam.

A successful participation in the integrated lecture consists of:

  • having obtained 50% of the exercise points
  • being allowed to participate in the integrated lecture "machine learning 1" without fulfilling any of the exclusion criteria described in the study regulations

The critera for a succesful participation in the optional compulsory courses differ by course.

For obtaining a certificate of participation, enrolment at the TU is neccessary in the form of a full enrolment, a Nebenhörerschaft or a Gasthörerschaft. For obtaining full accreditation, enrolment at the TU is neccessary in the form of a full enrolment or a Nebenhörerschaft.

More details on registration, exam modalities and accreditation can be found on the online part of the course.

Online course / ISIS

Link to the online course on ISIS

Course organization and participation in the online course are carried out over the ISIS system of the TU. For registration, a tubIT account is necessary. External participants who are enrolled at the TU (Nebenhörer and Gasthörer) can obtain an ISIS account at the tubIT office. For this, it is necessary to apply for an account at the tubIT-Laden with the Nebenhörer/Gasthörer enrolment certificate.

The discussion forums which are visible to all participants and the anonymous feedback possibilities can be used by all participants of the online part for asking questions and providing comments.

Exercises

Exercises are a central part of the integrated lecture "machine learning 1". In the exercise sessions, questions about the lecture topics are answered and the necessary theoretical and practical foundations for the exercises are explained, when required the solutions to the exercises are presented.

Coordination of the exercises, including submission and grading of the solved exercises, are done via the ISIS system of the TU. Each week, written and/or MATLAB programming problems are handed out.

Submission of the written exercises is also optionally possible in physical form.

MATLAB is installed on the systems administered by the IRB and can be accessed by executing the command
/home/ml/ml/bin/matlab
The computers of the IRB can only be accessed with an IRB accound which can be electronically requested from the IRB by users of a tubIT account and external participants.

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