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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BI-JUL.21 Programming in Julia Extent of teaching: 3C

Instructor: Kalvoda T. Completion: KZ

Department: 18105 Credits: 5 Semester: Z

Annotation:
The goal of the course is to introduce the students to Julia, a modern programming language and scientific programming environment. In the first part, the students will learn the basic concepts and features of Julia. The second part is focused on thematically diverse applications of tools available in Julia. Students will learn how to work effectively in the Julia environment and get an overview of its capabilities for solving problems in various fields, which they can encounter during their studies.

Lecture syllabus:
There are not lectures.

Seminar syllabus:

1. Introduction to scientific computing (motivation, history, present state, available tools, goals and problems).

2. Introduction to the Julia programming language (installation, packaging ecosystem, REPL, Juno IDE, Jupyter).

3. Flow control structures; type system: abstract, concrete, and parametric types; number types as an illustration.

4. Functions and methods, macros and metaprogramming.

5. Multidimensional arrays and matrix types.

6. Standard library (with emphasis on packages Linear algebra and Statistics).

7. Tools for parallel computation and profiling.

8. Modules, package and project development, documentation generation.

9. Visualization and manipulation with graphics (packages Plots, Gadfly, Images).

10. Data import and export (package DataFrames, various formats, reading, writing, data processing).

11. Optimization problems (package JuMP).

12. Differential equations (package DifferentialEquations).

13. Julia in Machine learning (selected topics from packages Flux and JuliaML).

Literature:

1. Balbaert I., Salceanu A. : Julia 1.0 Programming Complete Reference Guide. Packt Publishing, 2019. ISBN 978-1-83882-224-8.

2. Bezanson J., Edelman A., Karpinski S., Shah V. B. : Julia: A Fresh Approach to Numerical Computing. SIAM, 2017.

3. Nagar S. : Beginning Julia Programming-For Engineers and Scientists. Springer, 2017. ISBN 978-1-4842-3171-5.

Requirements:
Basics of programming and algorithmization as covered by BIE-PA1 and BIE-PA2 courses is assumed.

The course is also part of the following Study plans:

Study Plan Study Branch/Specialization Role Recommended semester

BI-UI.21 Artificial Intelligence 2021 (in Czech) PV 5

Page updated 25. 4. 2024, semester: Z,L/2023-4, Z/2019-20, Z/2024-5, L/2022-3, Z/2020-1, Z,L/2021-2, L/2020-1, Z/2022-3, L/2019-20, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška

BI-JUL.21	Programming in Julia			Extent of teaching:	3C
Instructor:	Kalvoda T.			Completion:	KZ
Department:	18105	Credits:	5	Semester:	Z

1.		Introduction to scientific computing (motivation, history, present state, available tools, goals and problems).
2.		Introduction to the Julia programming language (installation, packaging ecosystem, REPL, Juno IDE, Jupyter).
3.		Flow control structures; type system: abstract, concrete, and parametric types; number types as an illustration.
4.		Functions and methods, macros and metaprogramming.
5.		Multidimensional arrays and matrix types.
6.		Standard library (with emphasis on packages Linear algebra and Statistics).
7.		Tools for parallel computation and profiling.
8.		Modules, package and project development, documentation generation.
9.		Visualization and manipulation with graphics (packages Plots, Gadfly, Images).
10.		Data import and export (package DataFrames, various formats, reading, writing, data processing).
11.		Optimization problems (package JuMP).
12.		Differential equations (package DifferentialEquations).
13.		Julia in Machine learning (selected topics from packages Flux and JuliaML).

1.		Balbaert I., Salceanu A. : Julia 1.0 Programming Complete Reference Guide. Packt Publishing, 2019. ISBN 978-1-83882-224-8.
2.		Bezanson J., Edelman A., Karpinski S., Shah V. B. : Julia: A Fresh Approach to Numerical Computing. SIAM, 2017.
3.		Nagar S. : Beginning Julia Programming-For Engineers and Scientists. Springer, 2017. ISBN 978-1-4842-3171-5.