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Statistical Pattern Recognition

Prof. Thomas Brox

Statistical pattern recognition, nowadays often known under the term "machine learning", is the key element of modern computer science. Its goal is to find, learn, and recognize patterns in complex data, for example in images, speech, biological pathways, the internet. In contrast to classical computer science, where the computer program, the algorithm, is the key element of the process, in machine learning we have a learning algorithm, but in the end the actual information is not in the algorithm, but in the representation of the data processed by this algorithm.

This course gives an introduction in all tasks of machine learning: classification, regression, and clustering. In the case of classification, we learn a decision function from annotated training examples (e.g., a set of dog and non-dog images). Given a new image, the classifier should be able to tell whether it is a dog image or not. In regression we learn a mapping from an input function to an output function. Again this mapping is learned from a set of input/output pairs. Both classification and regression are supervised methods as the data comes together with the correct output. Clustering is an unsupervised learning method, where we are just given unlabeled data and where clustering should separate the data into reasonable subsets. The course is based in large parts on the textbook "Pattern Recognition and Machine Learning" by Christopher Bishop. The exercises will consist of theoretical assignments and programming assignments in Matlab.

The content of this course is complementary to the Machine Learning course offered by Joschka Boedecker and Frank Hutter. It absolutely makes sense to attend both courses if you want to specialize in Machine Learning. It also complements the Deep Learning course.

Lecture:
(2 SWS)
Thursday, 10:15-11:45
Building 52, Room 02-017

Exercises:
(2 SWS)
Monday, 14:15-15:45
Building 082 (Mensa), Pool 00-029
Contact persons: Huizhong Zhou, Anton Boehm
DiscussionForum

Exam: Written exam on Sept 17, 2019 14:00-15:00 in building 101.

Beginning: Lecture: Thursday, April 25, 2019
Exercises: Monday, April 29, 2019

ECTS Credits: 6

Recommended semester:

1 or 2 (MSc)
Requirements: Fundamental mathematical knowledge, particularly statistics.

Remarks: Full completion of all relevant theoretical and programming assignments is highly recommended.
The exam will consist of a mixture of binary choice questions and fields, in which you must fill your solution. To get an idea of the style of the exam you can have a look at the test exam for image processing and the test exam for optimization.
Relevance vector machines

Slides and Recordings

DateTopicSlidesRecordingsMaterial
25.4. Class 1: Introduction MachineLearning01.pdf MachineLearning01.mp4 Exercise 1
2.5.Class 2: Probability distributions MachineLearning02.pdf MachineLearning02.mp4 Exercise 2
9.5. Class 3: Mixture models, clustering, and EM MachineLearning03.pdf MachineLearning03.mp4 Exercise 3
16.5.Class 4: Nonparametric methods MachineLearning04.pdf MachineLearning04.mp4 Exercise 4
23.5.Class 5: Regression MachineLearning05.pdf MachineLearning05.avi Exercise 5
6.6. Class 6: Gaussian processes MachineLearning06.pdf MachineLearning06.avi Exercise 6
27.6.Class 7: Classification MachineLearning07.pdf MachineLearning07.mp4 Exercise 7
4.7.Class 8: Support vector machines MachineLearning08.pdf MachineLearning08.mp4 Exercise 8
11.7. Class 9: Projection methods MachineLearning09.pdf MachineLearning09.avi Exercise 9
18.7. Class 10: Inference in graphical models MachineLearning10.pdf MachineLearning10.mp4 Exercise 10
25.7.Class 11: Sampling methods MachineLearning11.pdf
MachineLearning11.mp4

Additional information and some helpful hints regarding the exercises can be found here.

Recordings from Olaf Ronneberger (2015):

Class 1: Introduction
Class 2: Probability distributions
Class 3: Mixture models, clustering, and EM
Class 4: Nonparametric methods
Class 5: Regression
Class 7: Classification
Class 8: Support vector machines
Class 9: Deep Learning
Class 10:Projection methods
Class 11:Sampling methods