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| || '''Termin:''' || Unregelmässig, siehe [[https://www.google.com/calendar/embed?src=pd5q3lv0b7833f3lnebbmqudjc%40group.calendar.google.com&ctz=Europe/Berlin|Terminübersicht]] || || '''Raum:''' || FR 1505 (Montag 10:00 - 12:00 Uhr) und FR 6043 (Mittwoch 10:00 - 12:00 Uhr) || || '''Dozent:''' || Prof. Dr. Klaus-Robert Müller || || '''Ansprechpartner:''' || [[http://www.user.tu-berlin.de/paulbuenau/|Paul von Bünau]] || || '''Anrechenbarkeit:''' || M.Sc. Modul Praktikum Maschinelles Lernen und Datenanalyse || |
|| '''Schedule:''' || Irregular, see [[https://www.google.com/calendar/embed?src=pd5q3lv0b7833f3lnebbmqudjc%40group.calendar.google.com&ctz=Europe/Berlin|calendar]] || || '''Room:''' || FR 6046 (Mondays) und FR 6043 (Wednesdays) || || '''Lecturer''' || Prof. Dr. Klaus-Robert Müller || || '''Contact:''' || [[http://www.user.tu-berlin.de/paulbuenau/|Paul von Bünau]] || || '''Module:''' || M.Sc. Module Praktikum Maschinelles Lernen und Datenanalyse || |
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| Im Praktikum Maschinelles Lernen und Datenanalyse soll der Prozess der explorativen Datenanalyse geübt werden. Der Schwerpunkt liegt auf den Themengebieten Visualisierung und Dimensionsreduktion, Klassifikation mit Neuronalen Netzen, Hidden-Markov-Modelle auf Genomdaten und Support-Vektor-Maschinen. Die Aufgaben sind kombinierte Programmier- und Simulationsaufgaben in Matlab. | The aim of this lab course is to practice the process of explorative data analysis and understand the main algorithms. The focus is on dimensionality reduction, clustering, classification and regression. For each assignment, a number of algorithms have to be implemented (in Matlab) and analyzed in experiments on real or synthetic data. Taking the [[Main/SS11_MatlabKurs|Matlab course]] and the [[Main/WS10_MaschinellesLernen1|Machine Learning lecture]] is highly recommended but not a formal prerequisite. |
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| Der Besuch des Kurses [[Main/SS11_MatlabKurs|Matlab Programmierung für Maschinelles Lernen und Datenanalyse]] wird empfohlen. Grundlegende Kentnisse des Maschinellen Lernen sind sehr hilfreich, bei grossem Interesse und Engagement aber keine zwingende Voraussetzung. | The lab course consists of two parts: a lecture (Mondays at 10.15am), which is held only when a new assignment is handed out, and a consultation on Wednesdays (10.15am) in all following weeks. Please have a look at the [[https://www.google.com/calendar/embed?src=pd5q3lv0b7833f3lnebbmqudjc%40group.calendar.google.com&ctz=Europe/Berlin|calendar]] for the exact schedule. |
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| Die Veranstaltung gliedert sich in zwei Teile: Eine Vorlesung, in der Methoden und Theorie der Aufgaben vermittelt werden (Montags 10:00 - 12:00 Uhr) und eine gemeinsame Sprechstunde (Mittwochs 10:00 - 12:00 Uhr), in der wir Fragen und Probleme besprechen, die bei der Bearbeitung der Aufgaben auftauchen. Die Termine finden nach Ankündigung (s.u.) statt. | Please register in the [[http://groups.google.com/group/mikiobraun-lehre|google group]] to receive announcements and ask questions. |
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| Weitere Informationen findet Ihr im [[attachment:Praktikum_ML_Info.pdf|Informationsblatt.]] | More information can be found in the german [[attachment:Praktikum_ML_Info.pdf|Informationsblatt.]] |
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| Für Anfragen, und Ankündigen wurde eine [[http://groups.google.com/group/mikiobraun-lehre|Google-Group]] eingerichtet. Man muß sich registrieren, um die Beiträge lesen zu können, aber jeder kann sich registrieren. | === Schedule === |
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| === Termine === [[https://www.google.com/calendar/embed?src=pd5q3lv0b7833f3lnebbmqudjc%40group.calendar.google.com&ctz=Europe/Berlin|Terminübersicht]] |
See [[https://www.google.com/calendar/embed?src=pd5q3lv0b7833f3lnebbmqudjc%40group.calendar.google.com&ctz=Europe/Berlin|calendar]] |
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| ==== Übungsblätter ==== | * [[attachment:ML_Praktikum_U01_en.pdf|Problem sheet #1 (Matlab)]], tests: [[attachment:U01_test_distmat.m|U01_test_distmat.m]],[[attachment:U01_test_mydet.m|U01_test_mydet.m]], [[https://ml01.zrz.tu-berlin.de/~paul/pass.pl?conf=ss11_prak01.conf|PASS]] |
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| ==== Daten ==== | * [[attachment:ML_Praktikum_U02_en.pdf|Problem sheet #2 (unsupervised learning)]], Data: [[attachment:flatroll.mat|flatroll.mat]], [[attachment:fishbowl_swissroll_correct.mat|fishbowl_swissroll_correct.mat]], [[attachment:U01_usps.mat|U01_usps.mat]]. [[attachment:lle_talk.pdf|LLE slides]]. Tests: [[attachment:U02_tests.zip|U02_tests.zip]], [[https://ml01.zrz.tu-berlin.de/~paul/pass.pl?conf=ss11_prak02.conf|PASS]] |
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| ==== Skript ==== | * [[attachment:ML_Praktikum_U03.pdf|Problem sheet #3 (Clustering, EM)]], [[attachment:ML_Praktikum_U03_en.pdf|English version]], Data: [[attachment:U03_5gaussians.dat|U03_5gaussians.dat]], [[attachment:U03_2gaussians.dat|U03_2gaussians.dat]], [[https://ml01.zrz.tu-berlin.de/~paul/pass.pl?conf=ss11_prak03.conf|PASS]], Tests: [[attachment:U03_tests.zip|U03_tests.zip]] |
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| [[attachment:guide.pdf|Skript zur Vorlesung (Stand: 14.4.2010)]] | * [[attachment:ML_Praktikum_U04.pdf|Problem sheet #4 (Classification: KRR, CV, ROC)]], [[attachment:ML_Praktikum_U04_en.pdf|English version]], [[https://ml01.zrz.tu-berlin.de/~paul/pass.pl?conf=ss11_prak04.conf|PASS]], Tests: [[attachment:U04_test_CV_krr.m|U04_test_CV_krr.m]], [[attachment:U04_datasets.tar.gz|Datasets]] |
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| ==== Ergebnisse ==== | * [[attachment:ML_Praktikum_U05_en.pdf|Problem sheet #5 (SVMs)]], [[attachment:U05_datasets.zip|Datasets]], [[https://ml01.zrz.tu-berlin.de/~paul/pass.pl?conf=ss11_prak05.conf|PASS]] |
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| || '''Matrikelnr.''' || '''Blatt 1''' || '''Blatt 2''' || '''Blatt 3''' || '''Blatt 4''' || '''Blatt 5''' || | ==== Literature ==== |
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| === Technik === | [[attachment:guide.pdf|Lecture notes (as of April 2010)]] |
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| Die Server {{{ {bolero,pepino,fiesta}.cs.tu-berlin.de }}} sind von aussen per ssh zu erreichen. | ==== Results ==== || '''Matrikelnr.''' || '''Sheet 1''' || '''Sheet 2''' || '''Sheet 3''' || '''Sheet 4''' || '''Sheet 5''' || ||305493||5||20||15||19||20|| ||307336||5||20||18||18||20|| ||306383||5||20||18||18||20|| ||331086||5||22||20||18||20|| ||310234||5||22||20||18||20|| ||310260||5||17||19||20||15|| ||311217||5||17||19||20||15|| ||315728||5||18||20||20||20|| ||314519||5||18||20||20||20|| ||327898||5||20||20||15||18|| ||329282||5||20||20||15||18|| ||329499||5||20||20||20||20|| ||329478||5||20||20||20||20|| ||334450||5||21||21||15||20|| ||335614||5||21||20||19||15|| ||336016||5||21||20||19||15|| === Access to Matlab === The servers {{{ {bolero,pepino,fiesta}.cs.tu-berlin.de }}} can be reached via ssh. |
Praktikum Maschinelles Lernen und Datenanalyse
Schedule:
Irregular, see calendar
Room:
FR 6046 (Mondays) und FR 6043 (Wednesdays)
Lecturer
Prof. Dr. Klaus-Robert Müller
Contact:
Module:
M.Sc. Module Praktikum Maschinelles Lernen und Datenanalyse
The aim of this lab course is to practice the process of explorative data analysis and understand the main algorithms. The focus is on dimensionality reduction, clustering, classification and regression. For each assignment, a number of algorithms have to be implemented (in Matlab) and analyzed in experiments on real or synthetic data. Taking the Matlab course and the Machine Learning lecture is highly recommended but not a formal prerequisite.
The lab course consists of two parts: a lecture (Mondays at 10.15am), which is held only when a new assignment is handed out, and a consultation on Wednesdays (10.15am) in all following weeks. Please have a look at the calendar for the exact schedule.
Please register in the google group to receive announcements and ask questions.
More information can be found in the german Informationsblatt.
Schedule
See calendar
Material
Problem sheet #1 (Matlab), tests: U01_test_distmat.m,U01_test_mydet.m, PASS
Problem sheet #2 (unsupervised learning), Data: flatroll.mat, fishbowl_swissroll_correct.mat, U01_usps.mat. LLE slides. Tests: U02_tests.zip, PASS
Problem sheet #3 (Clustering, EM), English version, Data: U03_5gaussians.dat, U03_2gaussians.dat, PASS, Tests: U03_tests.zip
Problem sheet #4 (Classification: KRR, CV, ROC), English version, PASS, Tests: U04_test_CV_krr.m, Datasets
Literature
Lecture notes (as of April 2010)
Results
Matrikelnr. |
Sheet 1 |
Sheet 2 |
Sheet 3 |
Sheet 4 |
Sheet 5 |
305493 |
5 |
20 |
15 |
19 |
20 |
307336 |
5 |
20 |
18 |
18 |
20 |
306383 |
5 |
20 |
18 |
18 |
20 |
331086 |
5 |
22 |
20 |
18 |
20 |
310234 |
5 |
22 |
20 |
18 |
20 |
310260 |
5 |
17 |
19 |
20 |
15 |
311217 |
5 |
17 |
19 |
20 |
15 |
315728 |
5 |
18 |
20 |
20 |
20 |
314519 |
5 |
18 |
20 |
20 |
20 |
327898 |
5 |
20 |
20 |
15 |
18 |
329282 |
5 |
20 |
20 |
15 |
18 |
329499 |
5 |
20 |
20 |
20 |
20 |
329478 |
5 |
20 |
20 |
20 |
20 |
334450 |
5 |
21 |
21 |
15 |
20 |
335614 |
5 |
21 |
20 |
19 |
15 |
336016 |
5 |
21 |
20 |
19 |
15 |
Access to Matlab
The servers {bolero,pepino,fiesta}.cs.tu-berlin.de can be reached via ssh.