Coding & Applied AI
Niveau
second cycle, Master
Learning outcomes of the courses/module
The graduate / the student:
* has an overview of programming languages
* knows the structure and structure of programs
* can create programs in a high-level language
* can use the development environment for a programming language
* can implement manageable problems in a program
* can use generative language models to produce program code
* has an overview of programming languages
* knows the structure and structure of programs
* can create programs in a high-level language
* can use the development environment for a programming language
* can implement manageable problems in a program
* can use generative language models to produce program code
Prerequisites for the course
English version available soon
Course content
* Programming languages (classification, principles, history)
* Detailed consideration of a modern programming language (e.g. Phyton)
* Overview and selection of a coding assistant
* Structure of programs
* Data types, operators, flow structures
* Development environment
* Typical work steps
* Setting up the development environment
* AI enabled Programming (input, debugging, execution)
* Independent planning and programming based on the programming languages taught in the lecture
* Development of AI-enhanced programs
* Detailed consideration of a modern programming language (e.g. Phyton)
* Overview and selection of a coding assistant
* Structure of programs
* Data types, operators, flow structures
* Development environment
* Typical work steps
* Setting up the development environment
* AI enabled Programming (input, debugging, execution)
* Independent planning and programming based on the programming languages taught in the lecture
* Development of AI-enhanced programs
Recommended specialist literature
Ziadé, T.; Expert Python programming learn best practices to designing, coding, and distributing your Python software; 2008
Nguyễn, Q.; Hands-on application development with pycharm : accelerate your python applications using practical coding techniques in pycharm; 2019
Anaya, M.; Clean code in Python : develop maintainable and efficient code; 2020
Perrotta P.; Machine Learning für Softwareentwickler: Von der Python-Codezeile zur Deep-Learning-Anwendung; 2020
Nguyễn, Q.; Hands-on application development with pycharm : accelerate your python applications using practical coding techniques in pycharm; 2019
Anaya, M.; Clean code in Python : develop maintainable and efficient code; 2020
Perrotta P.; Machine Learning für Softwareentwickler: Von der Python-Codezeile zur Deep-Learning-Anwendung; 2020
Assessment methods and criteria
Exam
Language
English
Number of ECTS credits awarded
5
Share of e-learning in %
20
Semester hours per week
2.5
Planned teaching and learning method
Lecture, individual work with software, group work, presentation and discussion of tasks
Semester/trimester in which the course/module is offered
1
Name of lecturer
Dipl.-Ing. Christoph Fröschl
Academic year
1. study year
Key figure of the course/module
DAT.1
Type of course/module
integrated lecture
Type of course
Compulsory
Internship(s)
not applicable