====== Lectures ======
  * Tuesdays, 12:45-14:15, KN:E-126
  * Lecturers: Petr Pošík (PP), Radek Mařík (RM)

List of {{:courses:be3m33ui:ui-competencies.pdf|competencies}} you should have after individual lectures. (Will be updated continuously.)

<WRAP todo>Work in prgress, update for the upcoming semester.</WRAP>

^ datum ^ č.t. ^ S/L ^ náplň ^
| 20.02.2018 |  1. | S |  |
| 27.02.2018 |  2. | L |  |
| 06.03.2018 |  3. | S |  |
| 13.03.2018 |  4. | L |  |
| 20.03.2018 |  5. | S |  |
| 27.03.2018 |  6. | L |  |
| 03.04.2018 |  7. | S |  |
| 10.04.2018 |  8. | L |  |
| 17.04.2018 |  9. | S |  |
| 24.04.2018 | 10. | L |  |
| 01.05.2018 | 11. | S | Svátek |
| 08.05.2018 | 12. | L | Svátek |
| 15.05.2018 | 13. | S |  |
| 17.05.2018 | 13. | S ^ Výuka jako v úterý ^
| 22.05.2018 | 14. | L |  |


^ Date ^ W# ^ Who ^ Contents ^ Materials ^
| 21.02.2017 |   1 | RM | AI, PR, learning and robotics. Decision tasks. Empirical learning. |{{:courses:be3m33ui:p01.learningbayesstrategy.pdf| P01.LearningBayesStrategy.pdf}} |
| 28.02.2017 |   2 | PP | Linear methods for classification and regression. | {{:courses:be3m33ui:b02linear-slides.pdf|Slides}}. {{:courses:be3m33ui:b02linear-handouts.pdf|Handouts}}. |
| 07.03.2017 |   3 | PP | Non-linear models. Feature space straightening. Overfitting. | {{:courses:be3m33ui:b03overfitting-slides.pdf|Slides}}. {{:courses:be3m33ui:b03overfitting-handouts.pdf|Handouts}}. |
| 14.03.2017 |   4 | PP | Nearest neighbors. Kernel functions, SVM. Decision trees. | {{:courses:be3m33ui:b04nonlinear-slides.pdf|Slides}}. {{:courses:be3m33ui:b04nonlinear-handouts.pdf|Handouts}}. |
| 21.03.2017 |   5 | PP | Bagging. Adaboost. Random forests. | {{:courses:be3m33ui:b05committees-slides.pdf|Slides}}. {{:courses:be3m33ui:b05committees-handouts.pdf|Handouts}}. |
| 28.03.2017 |   6 | PP | Probabilistic graphical models. Bayesian networks. | {{:courses:be3m33ui:b06bayesnets-slides.pdf|Slides}}. {{:courses:be3m33ui:b06bayesnets-handouts.pdf|Handouts}}. |
| 04.04.2017 |   7 | PP | Hidden Markov models. | {{:courses:be3m33ui:b07hmm-slides.pdf|Slides}}. {{:courses:be3m33ui:b07hmm-handouts.pdf|Handouts}}. |
| 11.04.2017 |   8 | PP | Expectation-Maximization algorithm. | {{:courses:be3m33ui:b08em-slides.pdf|Slides}}. {{:courses:be3m33ui:b08em-handouts.pdf|Handouts}}. |
| 18.04.2017 |   9 | RM | Planning. Planning problem representations. Planning methods. | {{:courses:be3m33ui:p09.aiplanning.pdf|Handouts}} |
| 25.04.2017 |  10 | RM | Scheduling. Local search. | {{:courses:be3m33ui:p10.schedulinghandouts.pdf|Handouts}} |
| 02.05.2017 |  11 |    ^ No lecture - schedule as on even Monday | |
| 09.05.2017 |  12 | RM | Constraint satisfaction problems. | {{:courses:be3m33ui:p11.constraintsatisfactionhandouts.pdf|Handouts}} |
| 16.05.2017 |  13 | PP | Neural networks. Basic models and methods, error backpropagation. | {{:courses:be3m33ui:b12nn-slides.pdf|Slides}}. {{:courses:be3m33ui:b12nn-handouts.pdf|Handouts}}. |
| 23.05.2017 |  14 | PP | Deep learning. Convolutional and recurrent NNs. | {{:courses:be3m33ui:b13deep-slides.pdf|Slides}}. {{:courses:be3m33ui:b13deep-handouts.pdf|Handouts}}. |

/*
  - The relation of artificial intelligence, pattern recognition, learning and robotics. Decision tasks, Empirical learning.
  - Linear methods for classification and regression.
  - Non-linear models. Feature space straightening. Overfitting.
  - Nearest neighbors. Kernel functions, SVM. Decision trees.
  - Bagging. Adaboost. Random forests.
  - * Graphical models. Bayesian networks.
  - * Markov statistical models. Markov chains.
  - * Expectation-Maximization algorithm.
  - Planning. Planning problem representations. Planning methods.
  - Scheduling. Local search.
  - Constraint satisfaction problems.
  - Neural networks. Basic models and methods, error backpropagation.
  - * Special neural networks. Deep learning.
  - Evolutionary algorithms (if time permits).
*/