Multimedia Computing and Computer Vision Lab

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WS 10/11: Maschinelles Lernen I

From Multimedia Computing Lab - University of Augsburg


Please register for the exam in
LectureReg


Image:Figure1.1.jpg
Figure from C. Bishop:Pattern Recognition and Machine Learning, Springer, 2006.

Overview

Instructors: Dr. Nicolas Cebron
Time Lecture: Tue, 15:45 - 17:15, Room: 1054 N
Time Exercise: Tue, 17:30 - 19:00, Room: 1054 N
Credits: 2 + 2 SWS, Schein, yes, LP 5
Exam: 22.02.2011, 15:30 - 17:30, Room: 1054 N
Multimedia Teilbereiche: Multimedia Methoden, Multimedia Anwendungen, Systemnahe Grundlagen von Multimedia

News

  • The results of the final exam can now be looked up in LectureReg.
  • You may inspect your final exams on Tuesday, February 1st, between 10:00 a.m. and 11 p.m. in room 1012N.
  • Vorlesung wird auf Deutsch gehalten (trotz der englischsprachigen Folien und Literatur)

Synopsis

This lecture will provide an introduction to the fields of machine learning and pattern recognition. They are concerned with the automatic discovery of regularities in data through the use of computer algorithms. With the use of these regularities, actions such as classifying the data into different categories can be taken.

Application areas range from robot navigation, classification of spam mails to speech recognition. In this lecture, we will emphasize a Bayesian perspective.

The following topics will be covered:

  • Probability Distributions
  • Linear Models for Regression/Classification
  • Neural Networks
  • Kernel Methods
  • Support Vector Machines

Exercise and Exam

  • There will be an exercise sheet every week
  • Solutions will be discussed during exercises on Tuesdays (no handing-in or revision of written solutions)
  • No admission requirements for the exam

Literature

  1. Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer Verlag. (http://research.microsoft.com/en-us/um/people/cmbishop/prml/)


Material

Iris Data
Segment Data
CVPR paper
Contact lenses data

Date Content Slides Exercise
19.10.2009 Organization / Introduction PDF
19.10.2009 Chapter 1: Introduction 1 PDF PDF
02.11.2010 Chapter 1: Introduction 2 PDF PDF
09.11.2010 Chapter 1/2: Introduction 3 / Probability Distributions PDF PDF
16.11.2010 Chapter 2: Probability Distributions / Clustering PDF PDF
23.11.2010 Chapter 3: Linear Regression PDF PDF
30.11.2010 Chapter 4: Linear Classification PDF PDF
07.12.2010 Chapter 5: Neural Networks PDF PDF
21.12.2010 Chapter 5: Neural Networks 2 PDF
11.01.2011 Chapter 6: Kernel Methods PDF PDF
18.01.2011 Chapter 7: Sparse Kernel Machines PDF PDF
25.01.2011 Chapter 14: Decision Trees PDF PDF
01.02.2011 Chapter 14: Combining Models PDF