CAS ETH in Data and Machine Learning  

Modules

Introduction to Programming – Dr. Lukas Fässler & Dr. Markus Dahinden

This course provides a practical introduction to some basic concepts and techniques for information processing and their practical applications. The programming languages used are Python and SQL.

Participants learn to develop mathematical models for real-world problems and solve them as small projects. The following programming concepts are covered: variables, data types, control structures, sequential data types, functions, and managing data with relational databases. Participants develop their programming skills through project-based work, online tutorials and individual support.
Participants who have already completed an equivalent programming course in another CAS will be given the opportunity to work on more advanced programming tasks.

Information, Data and Computers – Prof. Bernd Gärtner

This course provides an introduction to computer science concepts that are foundational for later work in the CAS and MAS programme.

We will cover how information is managed as data, and how we use computers to process data and generate new insights. Concrete questions we will address are: what is data, and how does it represent information? What is a computer, and how does it work? What is a computer program? What is a programming language? What is an algorithm? What kind of computers do we have today, and why? Through this, we will build a fundamental understanding of how computer and data science enable today's information society.

Data Science and Machine Learning – Dr. Andreas Streich & Dr. Marcel Lüthi

This course provides training in areas of data science and machine learning. The course is intended for managers and leaders who want to understand the typical workflow, fundamental techniques and key challenges of data science and machine learning to drive successful implementation.

We will cover the following topcs:

• The complete data processing pipeline from initial data understanding and cleaning through visualisation to deriving reliable, action-oriented insights.
• Using exploratory data analysis to develop hypotheses.
• Statistical measures and evaluation of hypotheses.
• Essentials of machine learning: The classical tasks in automatic learning from data, and common approaches to solve them, such as decision trees and neural networks.
• Model evaluation and selection using e.g. cross-validation.
• Foundations of deep Learning as drivers to understand the transformative nature of neural networks.
  
• Challenges and considerations: Potential pitfalls, threats, and ethical considerations.

AI and IT in Industry – Dr. Marc Brandis, Dr. Carlos Cotrini, Dr. Andreas Streich

This integration module links technical understanding of technology with business strategy based on a set of case studies from practice. Participants will explore how new information technologies such as machine learning and AI change different aspects of a business, and learn how to evaluate specific risks, costs, and benefits of such technologies. The module will shed light on success factors and common pitfalls when implementing new technologies and respective business changes, and it will specifically address the communication between technical experts and business management. The studied cases are currently planned to focus on artificial intelligence, IoT including edge and cloud computing, blockchain and distributed ledger technologies, and cybersecurity and data protection regulations (subject to change).