Kedma Hamelberg

kedma@hamelberg-ai.com

Teaching Philosophy & Experience

I approach teaching as an evidence-based, student-centered practice grounded in the power of education, human connection, and applied learning. My classrooms are designed to foster intellectual rigor, collaboration, and long-term professional value.

I intentionally learn my students’ names, actively engage them in discussion, and cultivate peer connection as a resource—both for navigating the course and as a source of lifelong career support.

My goal is to prepare students—whether full-time learners or working professionals—for the realities of today’s data-driven, ethically complex marketing landscape.

My teaching spans:

  • Designing new courses from scratch
  • Redesigning and coordinating existing programs
  • Delivering lectures and interactive tutorials
  • Supervising Master’s theses and mentoring career transitions

Courses taught:

  • Applied AI for Marketing (Master’s Elective)
  • Digital Marketing & Analytics (DMA) – Core course for both full-time and Executive Master’s students
  • Quantitative Data Analysis 2 (Bachelor’s, Economics)

Applied AI for Marketing (Master’s Elective)

This course trains students to critically assess and deploy AI in marketing contexts. It is structured around three core literacies:

  • Functional AI literacy: How AI works, conceptually and practically
  • Ethical AI literacy: How to navigate the ethical issues related to AI (e.g., Fairness, bias, transparency, and responsible use)
  • Rhetorical AI literacy: How natural language and AI-generated content persuades and performs

Key Features

  • Python-based tutorials in Google Colab
  • Deep learning models (e.g., BERT, transformers for text and image classification)
  • Business data from YouTube, websites, and social media
  • Predictive and causal modeling (e.g., DiD, logistic/linear regression)
  • Tutorials on single, multi-agent, and agentic AI
  • Live debates on political polarization, fairness, and explainability in AI
  • Collaborations with businesses to solve real challenges (e.g., Danone, OHRA, and Lucid Motors)

Sample Student Exercises

  • Design custom AI agents for marketing use cases
  • Compare zero-shot vs fine-tuned sentiment models
  • Use AI to classify emotions, values, or persuasive styles in brand messaging

Outcome

Students complete a strategic portfolio demonstrating fluency in AI tools, data analysis, and critical application to real-world challenges.

Digital Marketing & Analytics (DMA)

DMA is a foundational course for both traditional MSc students and Executive Master’s students with industry experience. It covers performance metrics, customer journeys, and omnichannel campaign design.

AI & Analytics Focus

  • Campaign strategy using AI-powered segmentation and persuasion
  • Analysis of structured and unstructured data (CRM, clickstream, scraped content)
  • Regression and classification modeling with real business datasets
  • Personalization, emotion-rich content, and multi-touch attribution frameworks

Innovations

  • AI applied to multimodal data and predictive analytics modules
  • Use of AI for language and paralanguage classification (topic modeling, sentiment, emotion, stance, etc.)
  • Ethical debates on AI surveillance, bias, and political ad targeting
  • Applied projects simulating real digital marketing pipelines

Outcome

Students leave the course with a portfolio-ready analytics toolkit and confidence in applying AI to omnichannel strategy and ROI modeling.

Quantitative Data Analysis 2 (Bachelor, Economics)

This statistics course introduces students to foundational quantitative methods using SPSS.

Sample of Topics Covered

  • Hypothesis testing and statistical inference
  • Linear and logistic regression
  • ANOVA and model diagnostics

Teaching Approach

I led lab sessions and offered individualized support, ensuring students built confidence in applying statistical concepts independently.

“Kedma was clear, structured, and incredibly supportive.”
“A true gem—the best tutorial teacher I had.”

🌟🌟🌟🌟🌟 Recognition & Student Feedback

  • Elected Best Lecturer by Executive students in the Digital Business Track
  • Consistently rated 8+/10 for clarity, engagement, and real-world relevance
  • Known for combining academic rigor with warmth, humor, and real-world orientation
  • Recognized for mentoring, personalized support, and helping students build lasting professional networks

“Thanks to your teaching style, I had fun while learning AI.”
“She creates a community—her feedback goes beyond the classroom.”
“Challenging, funny, and deeply committed to our growth.”