==== Lectures =====
**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  |  {{:courses:a4m33sad:sad_intro.pdf}}  |
|   2|  10.10. |  JK  | K-means algorithm, EM GMM, hierarchical clustering |  {{courses:ae4m33sad:clustering.pdf}}  |
|   3|  17.10.  |  JK  | Advanced clustering methods -- spectral clustering |  {{courses:a4m33sad: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  |  {{courses:ae4m33sad:apriori_eng.pdf}}  |
|   6|  7.11.  |  JK  | Frequent sequences and subgraphs  |  {{courses:a4m33sad:seq_graphs.pdf}}  |
|   7|  14.11.  |  JK  | Dimensionality reduction  |  {{dimreduction.pdf}}  |
|   8|  21.11.  |  FŽ  | Intro to supervised learning and computational learning theory  |  {{courses:a4m33sad:colt.pdf}}   |   
|   9|  28.11.  |  FŽ  | Continuing Lecture 1  | |    
|   10|  5.12.  |  FŽ  | Learning in propositional logic   |  {{courses:a4m33sad:pac-logic.pdf}}  |    
|  11|  12.12.  |  FŽ  | Continuing Lecture 3 |
|  12|  19.12.  |  FŽ  | Infinite Concept Spaces (only the 1st file required for exam)  |  {{courses:a4m33sad:infspaces.pdf}}, {{courses:a4m33sad:infspaces2.pdf}}  |   
|  13|  2.1.  |  FŽ  | Empirical testing of hypotheses  |  {{courses:a4m33sad:empirical.pdf}}    | 
|  14|  9.1.  |  FŽ  | Learning in predicate logic   |  {{courses:a4m33sad: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.
==== Tutorials =====

^  T ^  Date  ^  Deadline  ^  Contents  ^  Materials   ^    
|   1|  10.10.  |            | Introduction, program, requirements, SW; entrance test (prerequisite 33RPZ)  | {{:courses:a4m33sad:0_entrance_test.pdf|PDF}} |
|   2|  17.10.  |        | Missing Values and Outliers; Removing Outliers using k-means Algorithm | {{:courses:a4m33sad:1_assignment.pdf|PDF}} {{:courses:a4m33sad:1_assignment.zip|ZIP}} |
|   3|  24.10.  |       | EM Algorithm and Semi-Supervised Learning | {{:courses:a4m33sad:2_assignment.pdf|PDF}} {{:courses:a4m33sad:2_assignment.zip|ZIP}} |
|  4|  31.10.  |       | Spectral Clustering | {{:courses:a4m33sad:3_assignment.pdf|PDF}} {{:courses:a4m33sad:3_assignment.zip|ZIP}} |
|  5|  7.11.  |       | Frequent Itemsets, Association Rules | {{:courses:a4m33sad:4_assignment.pdf|PDF}} {{:courses:a4m33sad:4_assignment.zip|ZIP}} |
|  6|  14.11.  |       | Dimensionality reduction | {{:courses:a4m33sad:5_assignment.pdf|PDF}} {{:courses:a4m33sad:5_assignment1.zip|ZIP}} |
|   7|  21.11.  |            | Preparation for Test | {{:courses:a4m33sad:vzorovy_test.pdf|}} |
|   8|  28.11.  |            | Test |             
|   9|  5.12.  |   12.12.    | Underfitting and Overfitting, Learning Curves | {{:courses:a4m33sad:9_assignment.pdf|PDF}} {{:courses:a4m33sad:9_assignment.zip|ZIP}} |
|  10|  12.12.  |  19.12.     | Upper Bounds for Classification Errors | {{:courses:a4m33sad:10_assignment.pdf|PDF}} {{:courses:a4m33sad:10_assignment.zip|ZIP}} |
|  11|  19.12.  |  2.1.     | Learning k-term DNF using k-CNF | {{:courses:a4m33sad:11_assignment.pdf|PDF}} {{:courses:a4m33sad:11_assignment.zip|ZIP}} |
|  12|  2.1.  |   9.1.    | Parameter Selection using Cross-Validation | {{:courses:a4m33sad:13_assignment.pdf|PDF}} {{:courses:a4m33sad:13_assignment.zip|ZIP}} |
|  13|  9.1.  |           | Closing all submissions & Credit |  |