| University | ZHAW |
| Format | Part-time |
| Start | September 2025 |
| End | July 2028 |
| Duration | 6 semesters / 3 years |
Spring 2026: Now¶
| Course | Description |
|---|---|
| Stochastic Modeling | The ubiquitous presence of uncertainty and noise in the engineering sciences and the importance of randomized algorithms in computer and data science make it mandatory to understand and quantify random phenomena (read more). |
| Machine Learning | Machine learning (ML) emerged out of artificial intelligence and computer science as the academic discipline concerned with “giving computers the ability to learn without being explicitly programmed” (read more). |
| Machine Learning and Data in Operation | This module presents powerful techniques to manage the lifecycle of machine learning models, covering in particular baseline models, infrastructure (clusters, cloud, edge AI and resource management) and tooling (frameworks), model training and debugging, model evaluation and tuning, data management (sources, storage, versioning, privacy), systems testing (CI/CD) and explainability, deployment (batch, service, edge), monitoring (data drift) and continual learning (read more). |
| Advanced Statistical Data Analysis | One of the most used (statistical) models for inferential data analysis is the linear regression model. But it is restricted to a Gaussian distributed response and a linear function for linking the linear combination of predictors with the expected response (read more). |
| Academic Writing and Presenting | The goal of this module is to help students to further develop their knowledge and skills in academic writing and presenting through the medium of English (read more). |
Fall 2025: 1st Semester¶
| Course | Description |
|---|---|
| Bayesian Machine Learning | Bayesian statistics provides an alternative viewpoint to the classical ‘frequentist’ statistics by using a different, more subjective interpretation of probability (read more). |
| Predictive Modelling | This course will provide a self-study introductory review of the basic concepts of probability and statistics to understand probability distributions and to produce rigorous statistical analysis including estimation, hypothesis testing, and confidence intervals (read more). |
| Advanced Topics in Deep Learning | The purpose of this module is to enhance students’ understanding of deep learning techniques. (read more) |
| Machine Intelligence Lab | Seminar providing a broad overview of Reinforcement Learning based on the Deep RL Bootcamp (read more). |
| Management of Complex Processes | One of the biggest challenges encountered in management is recognizing opportunities and making use of them while giving consideration to the associated risks (read more). |