Coding & Applied AI
Niveau
second cycle, Master
Learning outcomes of the courses/module
The participants:
• have an overview of programming languages
• know 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
• have an overview of programming languages
• know 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
According to admission requirements
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
Semester hours per week
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