Applied Mathematics

NAME OF THE COURSE Applied Mathematics

Code

KTM100

Year of study

1.

Course teacher

Nives Baranović

Credits (ECTS)

4.0

Associate teachers

Type of instruction (number of hours)

P S V T

30

15

0

0

Status of the course

Mandatory

Percentage of application of e-learning

20 %

COURSE DESCRIPTION

Course objectives

Student are introduced to the ideas and methods of approximate solving algebraic and differential equations, interpolation and numerical integration, to concepts of the theory of probability and statistics and their application to particular example and tasks.
By developing a positive attitude toward learning, responsibility for own success and progress, and the acquisition of competencies described above, the students are expected to build a solid foundation for lifelong learning and further education.

Course enrolment requirements and entry competences required for the course

Students should have fundamental competencies related to the calculus.

Learning outcomes expected at the level of the course (4 to 10 learning outcomes)

After the passing the exam, students will be able to:
- use appropriate language, symbolic notation and graphic representation to describe the ideas and methods for numerical solving equations;
- apply the methods described above in the particular tasks;
- describe the concept and define the notions of probability theory;
- understand the concepts and apply the methods described above in real situations;
- define discrete and continuous random variables and their characteristics;
- properly interpret characteristics of random variables on particular examples;
- describe examples of important distribution and identify conditions for their use in problem solving;
- describe the concept and define the notions of statistics theory;
- use a computer and appropriate software as a tool in the statistical data processing;
- understand the process of statistical testing and parametric and non-parametric sample testing.

Course content broken down in detail by weekly class schedule (syllabus)

1st week: Introduction to the objectives and learning outcomes, curriculum, methods of evaluation and assessment criteria.
2nd week: Errors of approximate values. The types of errors. Sources of errors.
3rd week: Solving equations approximately. Graphical method. Bisection method. Iteration. Secant method. Tangent method.
4th week: Interpolation and approximation.
5th week: Numerical integration. Rectangular formula. Trapezium formula. Simpson’s formula.
6th week: Numerical solving of differential equations. Euler’s method. Taylor’s method.
Written exam.
7th week: Definition and properties of probability.
8th week: Conditional probability. Independence of events.
9th week: Random variables. Discrete and continuous random variables.
10th week: Independence of random variables.
11th week: Numerical characteristics of random variables. Mathematical expectation.
12th week: Dispersion. Mode and median. Moments. The coefficient of skewness and kurtosis.
13th week: Some important distributions. Binomial distribution. Poisson distribution. Normal distribution. Uniform distribution. Exponential distributions.
14th week: Basics of statistics. Population. Sample. Displaying data. The average value of the sample. Sample variance. Sample mode. Sample median.
15th week: Statistical testing. Parametric test. Nonparametric test. Χ2 test.
Written exam.

Format of instruction:

Student responsibilities

 

Screening student work (name the proportion of ECTS credits for eachactivity so that the total number of ECTS credits is equal to the ECTS value of the course):

Class attendance

0.0

Research

0.0

Practical training

0.0

Experimental work

0.0

Report

0.0

0.8

Essay

0.0

Seminar essay

0.0

 

 

Tests

0.0

Oral exam

1.6

 

 

Written exam

1.6

Project

0.0

 

 

Grading and evaluating student work in class and at the final exam

The first method: Engagement during semester 20% (E), written exam 40% (W) and oral exam 40% (O). During the semester, student can do additional task, write a seminar paper and express own knowledge through activities in class. The final score: z = 0.2(E) + 0.4(W) + 0.4(O).
The second method: written exam 500% and oral exam 50%; final: z = 0.5(W) + 0.5(O).

Required literature (available in the library and via other media)

Title

Number of copies in the library

Availability via other media

N. Baranović: Primijenjena matematika, materijal dostupan na moodlu Filozofskog fakulteta, 2014. https://paideia.ffst.hr/learning/login/index.php

0

e-learning

Optional literature (at the time of submission of study programme proposal)

I. Ivanšić: Numerička matematika, Element, Zagreb, 2002.
R. Scitovski: Numerička matematika http://www.mathos.unios.hr/nm/materijali/Num.pdf), 2004.
N. Elezović: Diskretna vjerojatnost, Element, Zagreb, 2008
N. Elezović: Matematička statistika. Statistički procesi, Element, Zagreb, 2008
E. Kreyszig: Advanced Engineering Mathematics, J. Wiley & Sons, New York,1999. (http://faculties.sbu.ac.ir/~sadough/pdf/Advanced%20Engineering%20Mathematics%2010th%20Edition.pdf)

Quality assurance methods that ensure the acquisition of exit competences

Quality assurance will be performed at three levels:
(1) University Level;
(2) Faculty Level by Quality Control Committee;
(3) Lecturer’s Level.

Other (as the proposer wishes to add)