====== Lectures ======
  * Tuesdays, 12:45-14:15, KN:E-301
  * Lecturers: Petr Pošík (PP), Radek Mařík (RM)

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

<WRAP round todo>The order of lectures is subject to change.</WRAP>

^ Date ^ W# ^ Who ^ Contents ^ Materials ^
| 19.02.2019 |   1 | PP | AI, PR, learning and robotics. Decision tasks. Empirical learning. | {{ :courses:ui:b01bayes-slides.pdf |Slides}}. {{ :courses:ui:b01bayes-handouts.pdf |Handouts}}. |
| 26.02.2019 |   2 | PP | Linear methods for classification and regression. | {{:courses:ui:b02linear-slides.pdf|Slides}}. {{:courses:ui:b02linear-handouts.pdf|Handouts}}. |
| 05.03.2019 |   3 | RM | Non-linear models. Feature space straightening. Overfitting. | {{:courses:ui:b03overfitting-slides.pdf|Slides}}. {{:courses:ui:b03overfitting-handouts.pdf|Handouts}}. |
| 12.03.2019 |   4 | PP | Nearest neighbors. Kernel functions, SVM. Decision trees. | {{:courses:ui:b04nonlinear-slides.pdf|Slides}}. {{:courses:ui:b04nonlinear-handouts.pdf|Handouts}}. |
| 19.03.2019 |   5 | PP | Bagging. Adaboost. Random forests. | {{:courses:ui:b05committees-slides.pdf|Slides}}. {{:courses:ui:b05committees-handouts.pdf|Handouts}}. |
| 26.03.2019 |   6 | PP | Neural networks. Basic models and methods, error backpropagation. | {{:courses:ui:b12nn-slides.pdf|Slides}}. {{:courses:ui:b12nn-handouts.pdf|Handouts}}. | 
| 02.04.2019 |   7 | PP | Deep learning. Convolutional and recurrent NNs. | {{:courses:ui:b13deep-slides.pdf|Slides}}. {{:courses:ui:b13deep-handouts.pdf|Handouts}}. |              
| 09.04.2019 |   8 | PP | Probabilistic graphical models. Bayesian networks. | {{:courses:ui:b06bayesnets-slides.pdf|Slides}}. {{:courses:ui:b06bayesnets-handouts.pdf|Handouts}}. |
| 16.04.2019 |   9 | PP | Hidden Markov models. | {{:courses:ui:b07hmm-slides.pdf|Slides}}. {{:courses:ui:b07hmm-handouts.pdf|Handouts}}. |                                         
| 23.04.2019 |  10 | PP | Expectation-Maximization algorithm. | {{:courses:ui:b08em-slides.pdf|Slides}}. {{:courses:ui:b08em-handouts.pdf|Handouts}}. |                             
| 30.04.2019 |  11 | RM | Planning. Planning problem representations. Planning methods. | {{:courses:ui:p09.aiplanning.pdf|Handouts}}|
| 07.05.2019 |  12 | RM | Constraint satisfaction problems. | {{:courses:ui:p11.constraintsatisfactionhandouts.pdf|Handouts}}  |
| 14.05.2019 |  13 |    | **No lecture. Schedule as on wednesday.** |  | 
| 21.05.2019 |  14 | RM | Scheduling. Local search. | {{:courses:ui:p10.schedulinghandouts.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).
*/