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--- courses:ae4m33bia:start [2012/10/11 11:59]
kubalik
+++ courses:ae4m33bia:start [2014/02/16 16:00]
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-====== AE4M33BIA -- Bio Inspired Algorithms ======
+====== AE4M33BIA (Bio Inspired Algorithms) ======
-===== Annotation =====
+Please see [[courses:a4m33bia:start|A4M33BIA]]
-The students will learn some of the uncoventional methods of computational intelligence aimed at solving complex tasks of search and optimization and modeling. Bio-inspired algorithms take advantage of analogies to various phenomena in the nature and society. The main topics of the subject are artificial neural networks and evolutionary algorithms.
-===== General Information =====
-  * [[courses:ae4m33bia:lectures|Lectures]]
-  * [[courses:ae4m33bia:labs|Labs/seminars]]
-  * [[courses:ae4m33bia:evaluation|Evaluation]]
-  * [[courses:ae4m33bia:literature|Literature]]
-  * [[https://cw.felk.cvut.cz/ulohy/|Upload system]]
-  * [[https://cw.felk.cvut.cz/forum/viewforum.php?id=45|Forums]]
-Lecturers: [[http://cyber.felk.cvut.cz/gerstner/gerstner/website/php/people-card.php?id=38&detailed=y|Jiří Kubalík]], [[http://cs.felk.cvut.cz/webis/people/drchajan.html|Jan Drchal]]
-==== Credit Allowance Conditions ====
-  * Participation and active work at all seminars.
-  * Submission of all seminar works during the course.
-  * Gathering of at least 50% of all points.
-==== Evaluation ====
-Maximal amount of points, which can be recieved during the course, is 100.
-points can be recieved during the semester:
-  * written report on the analysis of artificial neural networks: 10 points (min 5 points)
-  * written report on application of an evolutionary algorithm: 10 points (min 5 points)
-  * working program of the evolutionary algorithm: 10 points (min 5 points)
-  * oral presentation of the evolutionary algorithm: 5 points
-  * midterm test: 20 points (min 10 points)
-points can be recieved at the final exam:
-  * written exam test: 40 points (can be passed only with at least 20 points)
-  * optional oral exam: < -5, +5 > points
-^ Points      ^ Grade ECTS       ^ Evaluation    ^
-|  100 - 90    |  A  | excellent |
-|  89 - 80    |  B  | very good |
-|  79 - 70    |  C  | good |
-|  69 - 60    |  D  | satisfactory |
-|  59 - 50    |  E  | sufficient |
-|  49 and less   |  F  | failed |
-===== Links =====
-[[http://www.feld.cvut.cz/education/rozvrhy-ng/public/cz/predmety/12/82/p12822704.html|Schedule]]
-[[https://cw.felk.cvut.cz/forum/|Forum]]
-[[https://cmp.felk.cvut.cz/ulohy/|Submitting System]]
-===== Literature =====
-  * Rojas, R.: Neural Networks: A Systematic Introduction, Springer, 1996
-  * Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1998