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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.

Lecture:
(2 SWS)
Thursday, 10am-12am (10-12 Uhr)
Building 52, Room 02-017

Exercises:
(2 SWS)
Monday, 2pm-4pm (14-16 Uhr)
Building 082 (Mensa), Pool 00-029
Contact persons: Yassine Marrakchi, Tonmoy Saikia
DiscussionForum

Exam: Friday, September 28, 2018, 2pm-3pm s.t.
Building 101

Beginning: Lecture: Thursday, April 19, 2018
Exercises: Monday, April 23, 2018

ECTS Credits: 6

Recommended semester:

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

Remarks: Full completion of all relevant theoretical and programming assignments is highly recommended.
The next exam will be on 28.3.; it will be, like always, a written exam and will take 60 minutes.
Relevance vector machines

Slides and Recordings

DateTopicSlidesRecordingsMaterial
19.4. Class 1: Introduction MachineLearning01.pdf MachineLearning01.avi Exercise 1
26.4.Class 2: Probability distributions MachineLearning02.pdf MachineLearning02.avi Exercise 2
3.5. Class 3: Mixture models, clustering, and EM MachineLearning03.pdf MachineLearning03.avi Exercise 3
17.5.Class 4: Nonparametric methods MachineLearning04.pdf MachineLearning04.avi Exercise 4
7.6.Class 5: Regression MachineLearning05.pdf MachineLearning05.avi Exercise 5
14.6. Class 6: Gaussian processes MachineLearning06.pdf MachineLearning06.avi Exercise 6
21.6.Class 7: Classification MachineLearning07.pdf MachineLearning07.avi Exercise 7
28.6.Class 8: Support vector machines MachineLearning08.pdf MachineLearning08.avi Exercise 8
5.7. Class 9: Projection methods MachineLearning09.pdf MachineLearning09.avi Exercise 9
12.7. Class 10: Inference in graphical models MachineLearning10.pdf MachineLearning10.avi Exercise 10
19.7.Class 11: Sampling methods MachineLearning11.pdf
MachineLearning11.avi

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