AI Solution Specification, Design, and Operations (CE-AI)
AI Solution Specification, Design, and Operations (CE-AI)
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
AI Solution Specification, Design, and Operations provides deeper insights into key phases of AI (and data science) projects. It deepens foundations on data exploration and visualization, which are key to identify business opportunities and assess data. It also discusses on how to turn ideas into specifications and products. It also elaborates on computational aspects often relevant during operations but turning out to be useful also during early project phases. In summary topics are:
- Identifying and assessing opportunities data: Data exploration and visualization
- Requirements engineering for AI projects
- ML in Production - from models to products that are monitored and updated
- Efficient solutions: Distributed and parallel computing for machine learning and data processing
Teaching Method
- The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
Learning Results
??After successful completion of the course, students will
Professional competence (Advanced Machine Learning)
Professional competence (Data Visualisation)
Professional competence (Advanced Machine Learning)
- have deepened their understanding of the field of machine learning and acquired a large set of machine-learning techniques
- understand the challenges and solutions of processing large amounts of data
- gather requirements for projects in the field of machine learning
- be able to apply a diverse set of methods to address a number of machine learning problems
- critically reflect on analytical outcomes
- improve and mitigate self-inflicted errors
- use Python libraries for automated machine learning such as hyperopt, time series analysis
- understand large scale data processing frameworks such as Spark
Professional competence (Data Visualisation)
- be able to create visualisations that inform business decision making
- recognise the typical challenges of visualising large and complex data sets
- understand the main concepts, theories, and methods of data visualisation
- be able to use data-visualisation methods to analyse business problems, generate possible solutions, and compare these solutions in terms of their effectiveness and efficiency
- discuss challenges and benefits of statistical graphics
- help others in group work
- identify new challenges and independently develop viable solutions
- reflect on their own and others' visualisations
- be able to create graphs like bar charts, scatterplots, line charts, and heatmaps in R to represent various types of data sets visually
- be able to collect and prepare data before it can be visualised
Assessment Methods
Written exam (60min)