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.

Summary

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

Basic info

Winter semester 2017/2018

Where and when: KN:G-205 at Building G, Karlovo namesti, Monday 14:30-16:00

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.

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

Recommended literature

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

Exam

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.

Table of Contents