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This course introduces statistical decision theory and surveys canonical and advanced classifiers such as perceptrons, AdaBoost, support vector machines, and neural nets.

Basic info

Lecture plan 2019/2020

Week Date Lect. Slides Topic Wiki Additional material
1 23.9. JM pdf Introduction. Basic notions. The Bayesian recognition problem Machine_learning Naive_Bayes_classifier some simple problems
2 30.9. OD pdf Non-Bayesian tasks Minimax
3 7.10. JM pdf Parameter estimation of probabilistic models. Maximum likelihood method Maximum_likelihood
4 14.10. JM pdf Nearest neighbour method. Non-parametric density estimation. K-nearest_neighbor_algorithm
5 21.10. JM pdf Logistic regression Logistic_regression
6 28.10. (holiday, no lecture)
7 4.11. JM pdf Classifier training. Linear classifier. Perceptron. Linear_classifier Perceptron
8 11.11. JM pdf SVM classifier Support_vector_machine
9 18.11. JM pdf Adaboost learning Adaboost
10 25.11. JM pdf, pdf, CNN Mishkin Neural networks. Backpropagation Artificial_neural_network
11 2.12. JM pdf Cluster analysis, k-means method K-means_clustering K-means++
12 9.12. JM pdf Unsupervised learning. EM (Expectation Maximization) algorithm. Expectation_maximization_algorithm Hoffmann,Bishop, Flach
13 16.12. JM pdf Feature selection and extraction. PCA, LDA. Principal_component_analysis Linear_discriminant_analysis Veksler, Franc, ver1
14 6.1. JM pdf Decision trees. Decision_tree Decision_tree_learning Rudin@MIT
  • Duda R.O., Hart, P.E.,Stork, D.G.: Pattern Classification, John Willey and Sons, 2nd edition, New York, 2001
  • Schlesinger M.I., Hlaváč V.: Ten Lectures on Statistical and Structural Pattern Recognition, Springer, 2002
  • Bishop, C.: Pattern Recognition and Machine Learning, Springer, 2011
  • Goodfellow, I., Bengio, Y. and Courville, A.: Deep Learning, MIT Press, 2016. www


  • Only students that receive all credits from the lab work and receive (“zápočet”) can be examined. The labs contribute 50% to your final evaluation. Any extra credits beyond 50% will be considered at the final evaluation and may improve your mark.
  • The exam consists of two parts: written test and oral exam.
  • The written test lasts 60-90 minutes and contributes 40% to the final evaluation.
  • The questions used in the test are available here (if one can solve these questions, one will likely do well on the exam)
  • Oral part starts approximately 2 hours after the end of the test (the interim time is used to correct the tests). It contributes to the final evaluation by 10%.
  • To get grade “A” for the course, “A” or “B” result of the final written exam is required.
  • Oral exam questions are available here.

Exam dates

  • written part: January 22, 2020 in lecture room K1, 12:00
  • oral part: January 23 (the location will be specified)
courses/be5b33rpz/lectures/start.txt · Last modified: 2019/11/22 18:14 by drbohlav