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 the rationale and application of 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. Errors and exceptions
   1. Syntax errors
   2. Handling and raising exceptions
   3. User-defined exceptions
4. Important libraries
   1. Standard Python library
   2. Scientific calculations
   3. Speeding up Python
   4. Visualization
   5. Accessing databases
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

Machine learning is a field of scientific study concerned with algorithmic techniques that enable machines to learn performance on a given task via the discovery of patterns or regularities in exemplary data. Consequently, its methods commonly draw upon a statistical basis in conjunction with the computational capabilities of modern computing hardware.

This course aims to acquaint the student with the main branches of machine learning and provide a thorough introduction to the most widely used approaches and methods in this field.

1. Introduction to Machine Learning
   1. Regression & Classification
   2. Supervised & Unsupervised Learning
   3. Reinforcement Learning
2. Clustering
   1. Introduction to clustering
   2. K-Means
   3. Expectation Maximization
   4. DBScan
   5. Hierarchical Clustering
3. Regression
   1. Linear & Non-linear Regression
   2. Logistic Regression
   3. Quantile Regression
   4. Multivariate Regression
   5. Lasso & Ridge Regression
4. Support Vector Machines
   1. Introduction to Support Vector Machines
   2. SVM for Classification
   3. SVM for Regression
5. Decision Trees
   1. Introduction to Decision Trees
   2. Decision Trees for Classification
   3. Decision Trees for Regression
6. Genetic Algorithms
   1. Introduction to Genetic Algorithms
   2. Applications of Genetic Algorithms

Neural networks and deep learning approaches have revolutionized the fields of data science and artificial intelligence in recent years, and applications built on these techniques have reached or surpassed human performance in many specialized applications.

After a short review of the origins of neural networks and deep learning, this course will cover the most common neural network architectures and discuss in detail how neural networks are trained using dedicated data samples, avoiding common pitfalls such as overtraining.

The course includes a detailed overview of alternative methods to train neural networks and further network architectures which are relevant in a wide range of specialized application scenarios.

1. Introduction to Neural Network and Deep Learning
   1. The Biological Brain
   2. Perceptron and Multi-Layer Perceptrons
2. Network Architectures
   1. Feed-Forward Networks
   2. Convolutional Networks
   3. Recurrent Networks, Memory Cells and LSTMs
3. Neural Network Training
   1. Weight Initialization and Transfer Function
   2. Backpropagation and Gradient Descent
   3. Regularization and Overtraining
4. Alternative Training Methods
   1. Attention
   2. Feedback Alignment
   3. Synthetic Gradients
   4. Decoupled Network Interfaces
5. Further Network Architectures
   1. Generative Adversarial Networks
   2. Autoencoders
   3. Restricted Boltzmann Machines
   4. Capsule Networks
   5. Spiking Networks

The topic of artificial Intelligence (AI) has been addressed in computer science and cognitive science research since the 1950s; however, the meaning associated with the term has changed considerably over time. Having once been predominantly associated with logical calculus, reasoning, and planning, AI is now primarily interpreted in the context of deep networks of computational units. Despite these changes in approach, the important characteristic of AI continues to be the understanding and reproduction of cognitive abilities and functions by machines. This seminar strives to elucidate current research trends in AI. The students learn to independently analyze selected topics and case studies and link them with well-known concepts, as well as critically question and discuss them.

• The seminar covers current topics in artificial intelligence. Each participant must write a seminar paper on a topic assigned to him/her.

In this course, students learn terms and frameworks related to IT governance and IT compliance. First, a short introduction and an overview of the different aspects of IT governance and IT compliance are given; then, COBIT and IT basic protection are explained as two frameworks that are used in industrial practice. In addition, this course will introduce and discuss important legal frameworks and standards related to IT law.

1. IT Governance: Motivation and Challenges
   1. Governance and IT Governance
   2. Frameworks for IT Governance
   3. Typical IT Governance, Service Management, and Security Frameworks and Standards
2. COBIT Framework
   1. Overview of the Elements of COBIT
   2. Governance and Management Objectives
   3. Use of COBIT and COBIT Design Factors
   4. The Target Cascade of COBIT
3. IT Compliance
   1. Introduction to IT Compliance
   2. Examples of National and International Guidelines: Risk Management Standards and Frameworks
   3. IT Compliance: Typical Measures
4. Basic IT Protection According to BSI
   1. Overview and Structure
   2. Approach to IT Security Governance
   3. Usage Example of IT Security Governance
5. Introduction to IT Service Management
   1. What is Information Technology Service Management?
   2. What is ITIL® V4?
   3. What is ISO/IEC 20000-1:2018?
   4. Other ITSM Frameworks and Standards
6. IT Law
   1. Overview of Relevant Laws
   2. Protection of Intellectual Property
   3. IT Contracts
   4. Privacy

This course presents in depth national and international legal framework conditions of information processing for companies. After an examination of the differences between international legal systems, an introduction is given to those legal constructs which serve as a basis for the development of IT-relevant legislation. Subsequently, areas of law are discussed from the perspective of concrete application-oriented business scenarios, such as contract law, licensing and patenting.

An introduction to the EU legal system is followed by a detailed discussion of the European General Data Protection Regulation (GDPR), which gains increasingly international interest. This leads into a consideration of transnational legal systems and concludes with recommendations from supranational organizations.

1. Introduction
   1. General Concepts of Law
   2. Areas of Law
   3. International, Transnational and EU Law
   4. Definition and Scope of IT Law
   5. International, Transnational and European IT Law
   6. Law in Cross-Border Systems
2. E-Business and E-Commerce
   1. General Terms and Conditions of Business
   2. Electronic Commerce
   3. IT Contracts
   4. Intermediaries and Platforms
   5. Antritrust Law and IT
3. Intellectual Property
   1. Basic Concepts of Intellectual Property
   2. Copyright
   3. Software Copyright and Software Licensing
   4. Free and Open Licensing
   5. Patenting of Software
4. Privacy and Data Protection
   1. Basic Concepts of Privacy and Data Protection
   2. European General Data Protection Regulation (GDPR)
   3. Implementation Approaches of the GDPR
   4. International Data Transfer
5. Information Security and Computer Crime
   1. Information Security Law
   2. Electronic Signatures and Digital Identities
   3. Cybercrime
6. Online Media and Telecommunication
   1. Basics of Online Media Law
   2. Social Media and Freedom of Expression
   3. Fundamentals of Telecommunications Law
   4. Internet and Domain Law