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Jan 23rd exam test results


The exam will proceed in the indicated order. You can assume about 12 people being through in one hour. Everybody with 6 or less points gets classified F automatically. The rest continues to the oral exam.


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 2017/2018

Week Date Lect. Slides Topic Wiki Extra
1 2.10.JM pdf Introduction. Basic notions. The Bayesian recognition problem Machine_learning Naive_Bayes_classifier solved problems
2 9.10. JM pdf Non-Bayesian tasks Minimax
3 16.10.JM pdf Parameter estimation of probabilistic models. Maximum likelihood method Maximum_likelihood
4 23.10.OD pdf Nearest neighbour method. Non-parametric density estimation. K-nearest_neighbor_algorithm
5 30.10.JM pdf Logistic regression Logistic_regression
6 6.11.JM pdf Classifier training. Linear classifier. Perceptron. Linear_classifier Perceptron
7 13.11. JM pdf SVM classifier Support_vector_machine pdf
8 20.11. JM pdf Adaboost learning Adaboost
9 27.11. JM pdf Neural networks. Backpropagation Artificial_neural_network Flach, ver1
10 4.12. JM pdf Cluster analysis, k-means method K-means_clustering K-means++
11 11.12. JM pdf Unsupervised learning. EM (Expectation Maximization) algorithm. Expectation_maximization_algorithm Hoffmann,Bishop, Flach
12 18.12. JM pdf Feature selection and extraction. PCA, LDA. Principal_component_analysis Linear_discriminant_analysis Veksler, Franc, ver1
13 1.1. (holiday, no lecture)
14 8.1. 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.
courses/ae4b33rpz/lectures/start.txt · Last modified: 2018/01/23 19:13 by sochmjan