LECTURE

• [01] Lecture 1 (27.02.2026): Organizational issues, Introduction, Fisher's linear discriminant (two classes) T1 Polish slides
• [02] Lecture 2 (06.03.2026): Fisher's linear discriminant (cntd), Bayes classifier, naive Bayes classifier T1 Polish slides
• [04] Lecture 3 (20.03.2026): Linear and Quadratic Discriminant Analysis, k-nearest neighbors T1 Polish slides
• [05] Lecture 4 (27.03.2026): Classifier assessment methods T1 Polish slides, Hands-on 1: Handouts
• [06] Lecture 5 (10.04.2026): Classification trees T2 Polish slides
• [07] (17.04.2026): Test 1 (Exemplary test 1)
• [08] Lecture 6 (22.04.2026): Ensamble methods T2 Polish slides
• [09] Lecture 7 (08.05.2026): Support vector machines T2 Polish slides
• [10] Lecture 8 (12.05.2026): cluster analysis T3 Polish slides
• [11] (22.05.2026): Test 2 (Exemplary test 2)
• [12] Lecture 9 (29.05.2026): PCA and MDS T3 Polish slides
• [13] Lecture 10 (03.06.2026): factor analysis, CCA, kernel methods T3 Polish slides


• [14] (12.06.2026): Test 3 (Exemplary test 3)

T1 - covered by the first test
T2 - covered by the second test
T3 - covered by the third test

• Set of exam questions
  

LABS

• [00] Short python course
• [02] Lab 1 (06.03.2026): Fisher's linear discriminant (two classes and multiclass)
• [04] Lab 2 (20.03.2026): LDA, QDA nad naive Bayes classifiers
• [05] Lab 3 (27.03.2026): classifier assesment methods
• [06] Lab 4 (10.04.2026): classification trees
• [08] Lab 5 (22.04.2026): classification trees 2, grid search
• [10] Lab 6 (12.05.2026): ensamble methods
• [12] Lab 7 (29.05.2026): cluster analysis
• [13] Lab 8 (03.06.2026): PCA and MDS


• Assignments