Essentials

Content

The CAS Artificial Intelligence for Managers consists of 4 three days modules and a CAS thesis that you present at the very end of the programme.

• Introduction into business analytics and machine learning

   Prof. Dr. Martin Huber

  Discover how to apply business analytics and machine learning techniques to decipher complex business data, derive actionable insights, and drive strategic decisions. Develop understanding about the key-terminology around descriptive and predictive analytics and get familiar with no-code tools for you own analyses.

  Day 1: Basics of Data Analytics and Machine Learning

  • Understand the foundations of data-driven decision-making.
  • Explore descriptive analytics, data visualization, and diagnostic analytics.
  • Familiarize yourself with essential terminology and no-code tools for analysis.

  Day 2: Introduction to Machine Learning (Predictive Analytics) – Part I

  • Gain insights into artificial intelligence, machine learning, and deep learning.
  • Differentiate between supervised and unsupervised learning.
  • Dive into practical examples, including logistic regression and decision trees.

  Day 3: Introduction to Machine Learning (Predictive Analytics) – Part II

  • Explore advanced techniques like random forests and lasso regression.
  • Discover the power of boosting algorithms.
  • Learn how to assess and fine-tune algorithm performance with real-world examples.
• Supervised and unsupervised machine learning approaches

   Mr. Amir Tabakovic

  This in-depth, interactive, hands-on module delves into supervised and unsupervised learning, building upon the concepts introduced in the first module. The module reinforces understanding through practical exercises and exploration of real-world AI applications. Participants will be encouraged to engage with a variety of exercises that illuminate the intricacies of these machine learning paradigms.

  By the end of this module, students will not only have a more profound theoretical understanding but also be able to visualize how these technologies are applied in industry settings. The module’s composition is crafted to ensure that participants can immediately translate their enhanced knowledge into practice.

  Day 1: Supervised Machine Learning Content

  • • Review of Core Concepts: Algorithms in Supervised Learning (Linear Regression, Logistic Regression, Decision Trees, Random Forrest, Time Series, etc.).
  • Real-world Applications: Examining case studies where supervised learning provides strategic solutions in sectors like healthcare, finance, marketing and retail.
  • Hands-on Exercise: Building and evaluating a classifier using a supervised learning algorithm.

  Day 2: Unsupervised Machine Learning Content

  • Exploring Unsupervised Learning: Distinguishing between unsupervised learning and supervised learning.
  • Fundamental Techniques: Clustering, Anomaly Detection, Association Discovery, Topic Modelling, Dimensionality Reduction (PCA).

  Day 3: Comprehensive Machine Learning Use-Case

  • Integrative Machine Learning Challenge: A capstone exercise that combines elements from both supervised and unsupervised learning to solve a complex problem.
  • Data Preparation and Feature Engineering: Techniques for preparing a dataset for both types of learning algorithms.
  • Strategy and Implementation: Applying a systematic approach to select the appropriate algorithm based on the problem context.
  • Reflection and Discussion: Analyzing the outcomes of the machine learning models and discussing the insights and implications of the results.
• Generative AI

   Mr. Amir Tabakovic

  This comprehensive, hands-on introductory course on generative artificial intelligence is designed for participants without a technical background. The course aims to provide a solid foundation in the principles, techniques, and applications of generative AI. Concepts will be presented in a detailed and accessible manner, ensuring understanding for all students.

  Through step-by-step examples and organized content, the students will gain a strong understanding of generative AI and its real-world implications. The applied methodology ensures that the students can apply their newfound knowledge immediately.

  Day 1: Understanding Generative AI

  • Non-technical, but informative introduction to generative artificial intelligence, differentiating generative models from discriminative models, examples of generative AI applications in various domains
  • Important concepts of generative AI: Types of Generative Models (VAEs, GANs, Autoregressive Models), Pros and Cons of Different Generative Models

  Day 2: Harnessing Large Language Models

  • Large language models: basics, its applications and architectures of generative AI applications, pre-training and fine-tuning of LLMs
  • Applications of Generative AI in Business: Data Generation and Augmentation; Content Generation and Personalization; Design and Creativity

  Day 3: Advanced Applications and Ethical Considerations

  • Advanced GPTs application: Data Extraction, Transformations, Sentiment Analysis, Super-Prompts
  • Ethical and legal considerations in generative AI: biases and fairness concerns, intellectual property rights and plagiarism issues, privacy issues, ensuring responsible and ethical use of generative models

  Overall, the participants will leave Module 3 with a strong foundation in generative AI, practical skills, and ethical awareness, empowering them to work more effectively and efficiently in their individual roles and contribute positively to their organizations

• AI implementation and data governance in business

   Mr. Amir Tabakovic & Dr. Christopher Bruffaerts

  In Module 4, we delve into the practical aspects of implementing AI and machine learning in a corporate environment while ensuring robust data governance. Explore the challenges and opportunities of leveraging customer data, ethical considerations, and the development of data-driven business models.

  Day 1: Privacy & Analytics (ONLINE)

  Gain insights into the crucial intersection of privacy and analytics in the corporate world. This day will cover:

  • An informative introduction to the challenges of utilizing customer data for digital business models.
  • Key concepts such as Privacy-by-Design, Privacy Engineering, and Privacy Enhancing Technologies.
  • Exploration of relevant Privacy Enhancing Technologies, including AI-generated synthetic data, secure multiparty computation, federated learning, and data cleanrooms.
  • A deep dive into AI-generated synthetic data, exploring techniques like GANs, Variational Autoencoders, and Autoregressive Networks, and their business potential.
  • Real-world business cases and practical examples, including a predictive model performance comparison between synthetic and original data.

  Day 2: Machine Learning within an Organization (I)

  Learn the essential steps in implementing machine learning projects within a corporate setting. This day covers:

  • Translating business problems into machine learning projects and the significance of defining relevant metrics.
  • Reviewing modern approaches to running machine learning projects, with a focus on cloud services and AutoML features for rapid prototyping.

  Day 3: Machine Learning within an Organization (II)

  Build upon your knowledge from the previous days and gain a deeper understanding of machine learning within a corporate context. Topics covered include:

  • Illustrating concepts through use-cases covered in earlier modules, with a focus on addressing challenges such as ML bias, cloud costs, and regulatory and ethical considerations.
  • Taking an overarching view of the machine learning product, emphasizing return on investment (ROI).
  • Business cases and practical examples using real data and no-code tools.

  By the end of Module 4, participants will be equipped with the knowledge and tools to effectively implement AI solutions, navigate data privacy concerns, and harness the potential of machine learning within their organizations.

• CAS thesis presentation

  More information will be shared at that start of the CAS programme.