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

Winter semester 2018/2019

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, Vojtěch Franc (VF) xfrancv@cmp.felk.cvut.cz, Boris Flach (BF) flachbor@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.
• 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.