The lectures are given in English to all students.
| L | Date | Lecturer | Contents | Material |
|---|---|---|---|---|
| 1 | 3.10. | JK | Intro to unsupervised learning, cluster analysis - formalization | sad_intro.pdf |
| 2 | 10.10. | JK | K-means algorithm, EM GMM, hierarchical clustering | clustering.pdf |
| 3 | 17.10. | JK | Advanced clustering methods – spectral clustering | clustering_pokr.pdf |
| 4 | 24.10. | JK | Other clustering approaches – co-clustering, conceptual and semisupervised clustering | see the previous lecture |
| 5 | 31.10. | JK | Frequent itemsets, association rules, the Apriori algorithm | apriori_eng.pdf |
| 6 | 7.11. | JK | Frequent sequences and subgraphs | seq_graphs.pdf |
| 7 | 14.11. | JK | Dimensionality reduction | dimreduction.pdf |
| 8 | 21.11. | FŽ | Intro to supervised learning and computational learning theory | colt.pdf |
| 9 | 28.11. | FŽ | Continuing Lecture 1 | |
| 10 | 5.12. | FŽ | Learning in propositional logic | pac-logic.pdf |
| 11 | 12.12. | FŽ | Continuing Lecture 3 | |
| 12 | 19.12. | FŽ | Infinite Concept Spaces (only the 1st file required for exam) | infspaces.pdf, infspaces2.pdf |
| 13 | 2.1. | FŽ | Empirical testing of hypotheses | empirical.pdf |
| 14 | 9.1. | FŽ | Learning in predicate logic | predicate.pdf |
Additional reading: see http://www.cs.princeton.edu/~mona/MachineLearning_lecture_notes.html (especially lectures 4-8) for a detailed treatment of PAC learnability.
| T | Date | Deadline | Contents | Materials |
|---|---|---|---|---|
| 1 | 10.10. | Introduction, program, requirements, SW; entrance test (prerequisite 33RPZ) | ||
| 2 | 17.10. | Missing Values and Outliers; Removing Outliers using k-means Algorithm | PDF ZIP | |
| 3 | 24.10. | EM Algorithm and Semi-Supervised Learning | PDF ZIP | |
| 4 | 31.10. | Spectral Clustering | PDF ZIP | |
| 5 | 7.11. | Frequent Itemsets, Association Rules | PDF ZIP | |
| 6 | 14.11. | Dimensionality reduction | PDF ZIP | |
| 7 | 21.11. | Preparation for Test | vzorovy_test.pdf | |
| 8 | 28.11. | Test | ||
| 9 | 5.12. | 12.12. | Underfitting and Overfitting, Learning Curves | PDF ZIP |
| 10 | 12.12. | 19.12. | Upper Bounds for Classification Errors | PDF ZIP |
| 11 | 19.12. | 2.1. | Learning k-term DNF using k-CNF | PDF ZIP |
| 12 | 2.1. | 9.1. | Parameter Selection using Cross-Validation | PDF ZIP |
| 13 | 9.1. | Closing all submissions & Credit |