Code: AE4B33RPZ Pattern Recognition and Machine Learning
Lecturer: prof. Ing. Jiųķ Matas Ph.D. Weekly load: 2+2c Assessment: Z,ZK
Department: 13133 Credits: 6 Semester: W
Description:
The basic formulations of the statistical decision problem are presented. The necessary knowledge about the (statistical) relationship between observations and classes of objects is acquired by learning on the raining set. The course covers both well-established and advanced classifier learning methods, as Perceptron, AdaBoost, Support Vector Machines, and Neural Nets.
Contents:
1.The pattern recognition problem. Overview of the Course. Basic notions.
2.The Bayesian decision-making problem, i.e. minimization of expected loss.
3.Non-bayesian decision problems.
4.Parameter estimation. The maximum likelihood method.
5.The nearest neighbour classifier.
6.Linear classifiers. Perceptron learning.
7.The Adaboost method.
8.Learning as a quadratic optimization problem. SVM classifiers.
9.Feed-forward neural nets. The backpropagation algorithm.
10.Decision trees.
11.Logistic regression.
12.The EM (Expectation Maximization) algorithm.
13.Sequential decision-making (Wald“s sequential test).
14.Recap.
Recommended literature:
1.Duda, Hart, Stork: Pattern Classification, 2001.
2.Bishop: Pattern Recognition and Machine Learning, 2006.
3.Schlesinger, Hlavac: Ten Lectures on Statistical and Structural Pattern Recognition, 2002.
Keywords:
pattern recognition, statistical decision-making, machine learning, classification