The quest for artificial intelligence has captured humanity’s interest for many decades and has been an active research area since the 1960s. This course will give a detailed overview of the historical developments, successes, and set-backs in AI, as well as current and future developments. In addition to these introductory topics, the course will explore the legal, social, and ethical responsibilities associated with the development and deployment of AI. The course will introduce principles of knowledge representation, ranging from the development and use of expert systems in early AI systems to knowledge graphs and semantic web technologies. In order to understand cognitive processes, the course will give a brief overview of the biological brain and (human) cognitive processes and then discuss how insights from human cognition inform the design and function of AI systems. Particular focus will be given to Artificial Neural Networks. Moreover, core technologies that drive modern AI applications will be introduced. Key areas of focus will include machine learning for data-driven improvement, natural language processing for understanding and generating human language, and computer vision for interpreting and making decisions from visual information. The course will give an overview of a wide range of AI use cases, ranging from creative content creation with Generative AI, to autonomous systems and robotics, to the role of AI in cybersecurity. The use cases will highlight the potential of AI across application areas and industries, such as autonomous driving, medicine, finance, retail, and manufacturing. For these use cases, the specific challenges related to the responsible use of AI will be discussed.

1. History and Future of AI
   1. Historical Developments
   2. AI Winters
   3. Notable Advances in Artificial Intelligence
   4. Legal, Social, and Ethical Responsibilities in AI Development and Deployment
2. Knowledge Representation
   1. Overview of Expert Systems
   2. Introduction to Logic and Prolog
   3. Knowledge Graphs
3. Bridging Biology and Technology: Neuroscience, Cognition, and Neural Networks
   1. Neuroscience and the Human Brain
   2. Cognitive Science
   3. The Relationship Between Neuroscience, Cognitive Science, and Artificial Intelligence
   4. Artificial Neural Networks

4. Core Technologies of Artificial Intelligence
   1. Machine Learning
   2. Natural Language Processing (NLP)
   3. Computer Vision

5. Applications of Artificial Intelligence
   1. Applications of Computer Vision
   2. Natural Language Processing Use Cases
   3. Creative Content Creation with Generative AI
   4. Autonomous Systems & Robotics
   5. Predictive Analytics
   6. AI in Cybersecurity

The purpose of this course is to introduce students to the concepts involved in IT project management. This is achieved through the development of an understanding of the fundamental tenets of project management enhancing the students’ ability to apply their knowledge, skills and competencies in analyzing and solving IT project management problems. A special focus is put on the specifics of IT project organization, cost management and the human factor within IT projects.

1. Introduction: Characteristics of IT Projects
   1. Defining IT Projects
   2. Overview on Typical Roles and Phases of IT Projects
   3. Risks and Challenges of IT Projects
   4. Role of an IT Project Manager
2. Organizing the Work
   1. Project Breakdown Structure, Work Packages
   2. Prioritization
   3. Time Planning, Milestones, Gantt Charts
   4. Definition of Done
3. Cost Estimation and Controlling
   1. Challenges of Cost Estimation in IT Projects
   2. Estimation Techniques: 3-Point Estimation, Double Blind Expert Estimation, Function Points
   3. Cost Controlling Using Earned Value Analysis
   4. Risk Management
4. The Human Factor
   1. Vision Keeping
   2. Stakeholder Management
   3. Conflict Management
5. Organizing Small and Medium Projects
   1. Rational Unified Process (RUP)
   2. Agile Software Processes
   3. Scrum
   4. Plan-driven Project Management in Small Projects
6. Organizing Large Projects
   1. PMBOK Guide
   2. Prince2
   3. Multi Project Management
   4. Agile Software Processes in Large Projects
   5. Selection of the Appropriate Project Management Method

The focus of this course is to apply the project management knowledge gained previously in a practical portfolio project and reflect on the results. Students engage in this portfolio project and document the results, reflecting on the project management concepts they apply and the influence of these concepts on the success of the project.

• In this course, students perform and document a portfolio project in which they apply the project management topics covered in previous modules.

In the current decade, impressive advances have been achieved in the field of artificial intelligence. Several cognitive tasks like object recognition in images and video, natural language processing, game strategy, and autonomous driving and robotics are now being performed by machines at unprecedented levels of ability. This course will examine some of societal, economic, and political implications of these developments.

• The seminar covers current topics concerning the societal impact of artificial intelligence. Each participant must create a seminar paper on a topic assigned to him/her. A current list of topics is given in the Learning Management System.

Python is one of the most versatile and widely used scripting languages. Its clean and uncluttered syntax as well as its straightforward design greatly contribute to this success and make it an ideal language for programming education. Its application ranges from web development to scientific computing. Especially in the fields of data science and artificial intelligence, it is the most common programming language supported by all major data-handling and analytical frameworks. This course provides a thorough introduction to the language and its main features, as well as insights into rationale and application within important libraries related to data science and artificial intelligence and important adjacent concepts such as environments, testing, and version control.

1. Introduction to Python
   1. Data structures
   2. Functions
   3. Flow control
   4. Input / Output
   5. Modules & packages
2. Classes and inheritance
   1. Scopes and namespaces
   2. Classes and inheritance
   3. Iterators and generators
3. Important libraries
   1. Standard Python library
   2. Scientific calculations
   3. Machine learning libraries
   4. Visualization
   5. Accessing databases
4. Errors and exceptions
   1. Syntax errors
   2. Handling and raising exceptions
   3. User-defined exceptions
5. Working with Python
   1. Virtual environments
   2. Managing packages
   3. Unit and integration testing
   4. Documenting code
6. Version control
   1. Introduction to version control
   2. Version control with GIT

Building a successful data-based product requires a significant amount of high-quality code which needs to run in a professional production environment. This course starts by introducing the agile approaches Scrum and Kanban and then discusses the shift from more traditional software development approaches to the DevOps culture.

Special focus is given to the discussion and understanding of techniques and approaches for producing high-quality code such as unit and integration testing, test-driven development, pair programing, and continuous delivery and integration.

Since many software artefacts are accessed via APIs, this course introduces concepts of API design and paradigms.

Finally, this course addresses the challenges of bringing code into a production environment, building a scalable environment, and using cloud-cased approaches.

1. Agile Project Management
   1. Introduction to SCRUM
   2. Introduction to Kanban
2. DevOps
   1. Traditional lifecycle management
   2. Bringing development and operations together
   3. Impact of team structure
   4. Building a DevOps infrastructure
3. Software Development
   1. Unit & integration test, performance monitoring
   2. Test-driven development & pair programing
   3. Continuous delivery & integration
   4. Overview of relevant tools
4. API
   1. API design
   2. API paradigms
5. From Model to Production
   1. Building a scalable environment
   2. Model versioning and persistence
   3. Cloud-based approaches