====== Lectures ======
  * Tuesdays, 12:45-14:15. BBB room or 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.)

===== Links to BBB rooms/recordings =====
The table below shows the links to upcoming lecture rooms/passed lecture recordings.
You can also find the links to rooms and recordings in BRUTE, or in the [[https://portal.fl.cvut.cz|FELSight portal]].

{{CWREMOTESCHEDULE type=lecture course=be3m33ui}}

===== Schedule =====

<WRAP round todo>
The order of lectures is subject to change. \\
The PDFs are from previous years. They will be updated during the semester.
</WRAP>


^ Date ^ W# ^ Who ^ Contents ^ Materials ^
| 16.02. |   1 | PP | AI, PR, learning and robotics. Decision tasks. Empirical learning. | {{ :courses:ui:b01bayes-slides.pdf |Slides}}. {{ :courses:ui:b01bayes-handouts.pdf |Handouts}}. |
| 23.02. |   2 | PP | Linear methods for classification and regression. | {{:courses:ui:b02linear-slides.pdf|Slides}}. {{:courses:ui:b02linear-handouts.pdf|Handouts}}. |
| 02.03. |   3 | PP | Non-linear models. Feature space straightening. Overfitting. | {{:courses:ui:b03overfitting-slides.pdf|Slides}}. {{:courses:ui:b03overfitting-handouts.pdf|Handouts}}. |
| 09.03. |   4 | PP | Nearest neighbors. Kernel functions, SVM. Decision trees. | {{:courses:ui:b04nonlinear-slides.pdf|Slides}}. {{:courses:ui:b04nonlinear-handouts.pdf|Handouts}}. |
| 16.03. |   5 | PP | Bagging. Adaboost. Random forests. | {{:courses:ui:b05committees-slides.pdf|Slides}}. {{:courses:ui:b05committees-handouts.pdf|Handouts}}.|
| 23.03. |   6 | PP | Neural networks. Basic models and methods, error backpropagation. | {{:courses:ui:b12nn-slides.pdf|Slides}}. {{:courses:ui:b12nn-handouts.pdf|Handouts}}. | 
| 30.03. |   7 | PP | Deep learning. Convolutional and recurrent NNs. | {{:courses:ui:b13deep-slides.pdf|Slides}}. {{:courses:ui:b13deep-handouts.pdf|Handouts}}. |              
| 06.04. |   8 | PP | Probabilistic graphical models. Bayesian networks. | {{:courses:ui:b06bayesnets-slides.pdf|Slides}}. {{:courses:ui:b06bayesnets-handouts.pdf|Handouts}}. |
| 13.04. |   9 | PP | Hidden Markov models. | {{:courses:ui:b07hmm-slides.pdf|Slides}}. {{:courses:ui:b07hmm-handouts.pdf|Handouts}}. |                                         
| 20.04. |  10 | PP | Expectation-Maximization algorithm. | {{:courses:ui:b08em-slides.pdf|Slides}}. {{:courses:ui:b08em-handouts.pdf|Handouts}}. |                             
| 27.04. |  11 | RM | Planning. Planning problem representations. Planning methods. | {{:courses:ui:p09.aiplanning.pdf|Handouts}}|
| 04.05. |  12 | RM | Constraint satisfaction problems. | {{:courses:ui:p11.constraintsatisfactionhandouts.pdf|Handouts}}  |
| 11.05. |  13 | RM | Scheduling. Local search. | {{:courses:ui:p10.schedulinghandouts.pdf|Handouts}} | 
| 18.05. |  14 | PP | Reserve. Summarizing run through the topics. |  |




/*
  - 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).
*/