Advanced Machine Learning (SZF_168)
About Study Course
Objective
By the end of this course, students will learn to apply advanced machine learning techniques, including neural networks, LLMs, reinforcement learning, GANs, and autoencoders, to solve business problems. They will learn to build, deploy, monitor, and explain models, ensuring ethical use and strategic business impact.
Prerequisites
"Fundamentals of artificial intelligence and machine learning" course or equivalent
Learning outcomes
1.- Understanding advanced machine learning concepts, models, and algorithms.
- Familiarity with advanced neural network architectures(CNNs, RNNs, GANs, LLMs), their advantages and limitations.
- Understanding Reinforcement Learning.
- Knowledge of ethical considerations, bias, and fairness in AI.
- Understanding of model deployment and monitoring processes.
1.- Build and fine-tune advanced machine learning models.
- Use Natural Language Processing Techniques including prompt engineering.
- Deploy machine learning models.
- Use explainable AI techniques like SHAP and LIME for model interpretability.
1.- Choose and apply machine learning solutions to improve business decision-making and efficiency.
- Communicate model results and insights effectively to non-technical stakeholders.
- Ensure ethical AI practices are integrated.
- Adapt machine learning models in dynamic environments.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Digital Strategy and Artificial Intelligence Management | 2 | Master's | Required | ||
| Digital Strategy and Artificial Intelligence Management | 2 | Master's | Required |
