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                  |         Lecture (2/401-a)                   |    Practice (2/310)
                  |        Tuesday 14:50-16:10                  |   Friday 13:20-14:40
------------------|--------------------------------------------------------------------------------------------
                  | Oct                     Nov         Dec     |  Nov       Dec
                  | 01  08  15  22  29  05  12  19  26  02      |  29   29   06   20 (exam)
------------------|--------------------------------------------------------------------------------------------
Jung Younsung     | o   o   o   o   o   -   o   -   -   -       |  -	-	 o    o
Torgashava Olga   | o   o   o   o   o   -   o   o   o   -       |  o    o    -    o
Sophia Belous     | o   o   o   o   o   o   o   o   o   o       |  o    o    -    o
Alxander Kuchur   | o   o   o   o   o   o   o   o   o   o       |  o    o    o    o
Koroliuk Andrei   | o   o   o   -   -   o   o   o   o   o       |  o    o    o    -
                  | o   -   -   -                               |  Notice: on 29 Nov the room will not be
                  | o   -   -   -                               |          available, but I will give you
                  | o   -   -   -  In case I need an exam       |          a home work, and count it as
                  | o   -   -   -     to evaluate you to give   |          one unit of practice. (Note that
                  | o   -   -   -     a credit for this cours,  |          4 units should be completed
                  | o   -   -   -  exam will be given on 20 Dec.|          to get a credit of this course.)
                  | o   -   -   -     after practice time       |
                  | o   -   -   -                               |  Sorry, but I won't be in Brest on 13 Novemer.
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[Content of talk]
10/01 The key equation of Bayesian probability: prob(hypothesis|evidence) = ?
10/08 Baeysian Classification with 1-D Gaussian pdf.
10/15 Baeysian Classification with 2-D Gaussian pdf and how to draw the border line
10/22 Baeysian Classification with 2-D Gaussian pdf and higher dimensionality
10/29 Bayesian network some simple examples  
11/05 Bayesian network: How to calculate p(A,B,C,D,E,...) = ?
11/12 Bayesian network: Inference of probability of one variable from all/partial information
11/19 Bayesian Decision Network: chance-node, decision-node, utility-node (how happy will be the decision?)
11/26 Bayesian Decision Network: algorithm to get decision table for one decision-node
12/02 Bayesian Decision Network: algorithm to get decision table for more than one decision-node
[practice]
11/29 Try to create random 50 1-D points of two families  each follows 1-D Gaussian distribution function
      Explore an application that displayes two families  each follows 2-D Gaussian distribution function
11/29 Try to create random 100 3-D points of two families each follows d-D Gaussian distribution function (Home work)
12/06 Explore an application that enables us to display any Bayesian network
12/20 Try to create "Decision Table" of some Bayesian Decision network using the application give on 12/06