The elective course “Ethics of Computer Science and AI” covers the ethical, social, and legal implications of the development and use of computer science and artificial intelligence (AI) technologies. The course explores topics such as privacy, security, bias, discrimination, accountability, transparency, and the impact of these technologies on society. It also examines different ethical frameworks and principles and how they apply to the design and deployment of computer science and AI systems. Additionally, the course involves case studies and discussions to foster critical thinking and ethical decision-making skills.

The elective course in “Software engineering” covers the principles and practices of designing, developing, and maintaining software systems. Students learn about software development methodologies, such as agile and waterfall, as well as programming languages, software testing, and project management. The curriculum also includes software architecture, database design, and human-computer interaction. Students work on projects individually or in teams, gaining hands-on experience with software development tools and techniques. The goal of the course is to prepare students for careers in the software industry or further study in computer science.

The course in Mobile Programming covers the development of mobile applications for iOS and Android platforms. It includes topics such as app development frameworks, user interface design, data management, debugging, and testing. Students learn programming languages (such as Java, Kotlin, Swift, and Objective-C), as well as mobile development tools such as Xcode and Android Studio. The course also covers advanced topics such as mobile security, cloud integration, and cross-platform development. Students are required to develop a mobile app as a project to demonstrate their skills.

The course in “Front-end Programming” covers the essential concepts and tools required for building responsive and interactive web applications. Students will learn the fundamentals of HTML, CSS, and JavaScript and how to use libraries and frameworks such as React and Angular to create dynamic user interfaces. Additionally, the course covers topics such as web accessibility, user experience design, and web performance optimization. Students will also develop practical skills through hands-on projects and assignments, allowing them to build a portfolio of real-world applications.

In “Cloud Computing Automation and Ops”, students learn about the fundamentals of cloud computing, different cloud service models, and deployment models. They also learn how to automate tasks using various tools and techniques, including scripting and programming languages, automation frameworks, and configuration management tools. The course also covers cloud infrastructure monitoring, troubleshooting, and optimization to ensure the high availability and performance of cloud-based applications and services. Finally, students learn about security, compliance, and governance in cloud environments.

The elective course in “Computer Vision” covers the fundamental principles and techniques for interpreting and analyzing digital images and videos using computers. This includes topics such as image processing, feature extraction, object recognition, machine learning, deep learning, and 3D vision. Students will learn how to develop computer algorithms to perform tasks such as image classification, object detection, and tracking. Applications of computer vision can be found in a wide range of fields including robotics, medical imaging, autonomous vehicles, and security.

The course “Complex Networks and their Applications” covers the study of complex systems and networks, with a focus on their mathematical properties and real-world applications. Topics include network structure and dynamics, statistical properties of networks, network models, community detection, centrality measures, and network algorithms. The course also explores applications of network science in various fields, such as social networks, biological networks, transportation networks, and communication networks. Students will gain an understanding of the principles behind network analysis and will learn to apply network science tools and techniques to solve real-world problems.

The course in Machine Learning covers the foundational concepts and techniques of building and training intelligent computer systems. This includes topics such as probability and statistics, supervised and unsupervised learning algorithms, deep learning, natural language processing, computer vision, and reinforcement learning. Students also learn about the ethical and societal implications of machine learning, as well as practical applications in various industries such as finance, healthcare, and marketing. The course involves both theoretical lectures and hands-on projects that require programming and data analysis skills.