(Syllabus)
 
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{{CourseHeader
 
{{CourseHeader
     | code = 2-IKV-102
+
     | code = 2-IKVa-102
     | title = Introduction to Computational Intelligence
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     | title = Mathematics for Cognitive Science
 
}}
 
}}
 
__TOC__
 
__TOC__
  
The lectures will provide students with basics of propositional and
+
The lectures will provide students with the basics of propositional and
predicate logic, linear algebra, mathematical analysis, and probability that are important for
+
predicate logic, linear algebra, mathematical analysis, and the probability that are important for
 
the study of informatics and its role in (computational) cognitive science. At the same time,
 
the study of informatics and its role in (computational) cognitive science. At the same time,
 
students will learn about mathematical culture, notation, way of thinking and expressing
 
students will learn about mathematical culture, notation, way of thinking and expressing
 
oneself.
 
oneself.
  
{{Infolist|2-IKV-102|Course information sheet >}}
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== Course schedule ==
 +
{| class="alternative table-responsive"
 +
!Type
 +
!Day
 +
!Time
 +
!Room
 +
!Lecturer
 +
|-
 +
|Lecture
 +
|Tuesday
 +
|14:50 - 16:20
 +
|I-9
 +
|[https://dai.fmph.uniba.sk/w?title=Maria_Lucka/en Mária Lucká]
 +
|-
 +
|Exercise
 +
|Thursday
 +
|12:20 - 14:50
 +
|I-9
 +
|[https://dai.fmph.uniba.sk/w?title=Maria_Lucka/en Mária Lucká]
 +
|}
  
[[{{NAMESPACE}}:{{ROOTPAGENAME}}/sk|This page is available only in Slovak]]
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==How to join the course==
 +
I'll add all students who sign up for this course in the AiS (Academic Information System). The course will be held in a classical, in-person form.
 +
 
 +
== Syllabus ==
 +
<ol>
 +
<li>Basics of mathematical analysis: functions, differential calculus</li>
 +
<li>Basics of linear algebra: matrices and vectors, operations </li>
 +
<li>Basics of probability: likely and not likely, unconditional and conditional probability </li>
 +
</ol>
 +
 
 +
== References ==
 +
 
 +
* Discrete structures with contemporary applications / Stanoyevitch A. CRC Press, Taylor & Francis Group, 2011.
 +
* A First Course in Real Analysis. Second Edition. / Protter, M.H. & Morrey, C.B. Springer-Verlag , 1991.
 +
* Basics of Mathematical Functions: https://www.khanacademy.org/math/algebra/algebra-functions
 +
* [https://ocw.mit.edu/ans7870/resources/Strang/Edited/Calculus/Calculus.pdf Calculus] / Gilbert Strang. Massachusetts Institute of Technology: Wellesley-Cambridge Press.
 +
* [https://www.math.ubc.ca/~carrell/NB.pdf Fundamentals of Linear Algebra] / James B. Carrell. Canada: University of British Colombia, 2005.
 +
* Artificial Intelligence: A Modern Approach / Stuart Russell and Peter Norvig. The USA: Pearson, 2010.
 +
 
 +
== Course grading ==
 +
<b>To be classified student has to achieve at least 50% of every activity:</b>
 +
 
 +
====THREE EXAM TESTS:====
 +
*form: 60 minutes writing test
 +
*terms: to be announced
 +
*goal: progress definition
 +
*weight: 20 % each
 +
 
 +
====ATTENDANCE, ACTIVITY====
 +
*form: classwork (solving problems and schoolmate’s help)
 +
*term: every lecture and exercise
 +
*goal:  regular preparation, cooperation, and virtual-social activity
 +
*weight: 10%
 +
 
 +
====FINAL EXAM====
 +
*form: 90 minutes writing test
 +
*term: January, February 2022
 +
*goal: course output 
 +
*weight: 30%
 +
 
 +
 
 +
<b>OVERALL GRADING:</b>  A > 90%, B > 80%, C> 70%, D > 60%, E > 52%

Aktuálna revízia z 18:04, 10. september 2023

Mathematics for Cognitive Science 2-IKVa-102

The lectures will provide students with the basics of propositional and predicate logic, linear algebra, mathematical analysis, and the probability that are important for the study of informatics and its role in (computational) cognitive science. At the same time, students will learn about mathematical culture, notation, way of thinking and expressing oneself.

Course schedule

Type Day Time Room Lecturer
Lecture Tuesday 14:50 - 16:20 I-9 Mária Lucká
Exercise Thursday 12:20 - 14:50 I-9 Mária Lucká

How to join the course

I'll add all students who sign up for this course in the AiS (Academic Information System). The course will be held in a classical, in-person form.

Syllabus

  1. Basics of mathematical analysis: functions, differential calculus
  2. Basics of linear algebra: matrices and vectors, operations
  3. Basics of probability: likely and not likely, unconditional and conditional probability

References

  • Discrete structures with contemporary applications / Stanoyevitch A. CRC Press, Taylor & Francis Group, 2011.
  • A First Course in Real Analysis. Second Edition. / Protter, M.H. & Morrey, C.B. Springer-Verlag , 1991.
  • Basics of Mathematical Functions: https://www.khanacademy.org/math/algebra/algebra-functions
  • Calculus / Gilbert Strang. Massachusetts Institute of Technology: Wellesley-Cambridge Press.
  • Fundamentals of Linear Algebra / James B. Carrell. Canada: University of British Colombia, 2005.
  • Artificial Intelligence: A Modern Approach / Stuart Russell and Peter Norvig. The USA: Pearson, 2010.

Course grading

To be classified student has to achieve at least 50% of every activity:

THREE EXAM TESTS:

  • form: 60 minutes writing test
  • terms: to be announced
  • goal: progress definition
  • weight: 20 % each

ATTENDANCE, ACTIVITY

  • form: classwork (solving problems and schoolmate’s help)
  • term: every lecture and exercise
  • goal: regular preparation, cooperation, and virtual-social activity
  • weight: 10%

FINAL EXAM

  • form: 90 minutes writing test
  • term: January, February 2022
  • goal: course output
  • weight: 30%


OVERALL GRADING: A > 90%, B > 80%, C> 70%, D > 60%, E > 52%