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

Winter semester 2020/2021

Due to the covid-19 situation, the lectures will be given online, via zoom. All students enrolled in KOS will be sent a link at 12:30.

Where and when: KN:G-205 at Building G, Karlovo namesti, Monday 12:45-14:15

Teaching: Jiří Matas (JM) matas@cmp.felk.cvut.cz, Ondřej Drbohlav (OD) drbohlav@cmp.felk.cvut.cz

• 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. A threshold for passing the exam is set, usually between 5-10 points, depending on the complexity of the test.
• 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), or if the number of students is large, the following day.. 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.