The evaluation and definition of use cases is the fundamental groundwork from which the projects can be defined. This does not only include the scope and technical requirements of a project but also how value can be derived from the project.

A crucial aspect is the definition of what makes a project successful, both in terms of a technical evaluation as well as a business centric perspective and how the status quo can be monitored effectively during the progress of a project.

The course also discusses how to avoid common fallacies and understand the implications of introducing data-driven decisions into traditional management structures.

1. Use Case Evaluation
   1. Identification of Use Cases
   2. Specifying Use Case Requirements
   3. Data Sources and Data Handling Classification
2. Model-centric Evaluation
   1. Common Metrics for Regression and Classification
   2. Visual Aides
3. Business-centric Evaluation
   1. Cost Function and Optimal Point Estimators
   2. Evaluation Using KPIs
   3. A/B Test
4. Monitoring
   1. Visual Monitoring Using Dashboards
   2. Automated Reporting and Alerting
5. Avoiding Common Fallacies
   1. Cognitive Biases
   2. Statistical Effects
   3. Change Management: Transformation to a Data-driven Company

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

In the course “Project: AI Use Case”, students choose a project task in accord with their tutor from a variety of options. The goal is to prototypically implement an artificial intelligence model or system in a suitable development environment. The choice of approach, the system or software implemented, and the resulting performance on the task are to be reasoned about, explained, and documented in a project report. To this end, students make practical use of the methodological knowledge acquired in the previous courses by applying them to relevant real-world problems.

• In this project course the students work on a practical implementation of an artificial intelligence use case of their choosing. All relevant artifacts like use case evaluation, chosen implementation method, code, and outcomes are to be documented in the form of a written project report.