Machine Learning for Smarter Innovation

Bridging Machine Learning with Design Thinking

Innovation Diamond
Innovation Diamond
Learning Journey
Learning Journey
ML Pipeline
ML Pipeline

Machine Learning for Smarter Innovation

A BSc-level course bridging Machine Learning and AI with Design Thinking methodologies.

The Innovation Diamond

Our core concept visualizes the innovation process: expansion from 1 challenge through divergent thinking to 5000 possibilities, then convergence through ML filtering to 5 strategic solutions.

Course Topics

ML Foundations

Introduction to machine learning concepts and the learning journey

Beginner 60 minutes

Supervised Learning

Learning from labeled examples to make predictions

Beginner 60 minutes

Unsupervised Learning

Discovering patterns in data without predefined labels

Beginner 45 minutes

Neural Networks

Deep learning architectures for complex pattern recognition

Intermediate 75 minutes

Clustering

Discovering natural groupings in data for customer segmentation

Intermediate 90 minutes

NLP & Sentiment Analysis

Extracting meaning and emotion from text data

Intermediate 90 minutes

Classification

Categorizing data into predefined classes with decision trees

Intermediate 90 minutes

Generative AI

Creating content with large language models and AI systems

Intermediate 90 minutes

Topic Modeling

Discovering abstract topics in document collections

Intermediate 75 minutes

Responsible AI

Ethics, fairness, and explainability in machine learning

Intermediate 75 minutes

Structured Output

Generating reliable, formatted AI responses

Intermediate 60 minutes

Validation & Metrics

Evaluating and measuring model performance

Intermediate 75 minutes

A/B Testing

Statistical experimentation for data-driven decisions

Intermediate 75 minutes

Finance Applications

ML applications in financial services and risk management

Advanced 90 minutes

Innovation Diamond

Capstone presentation: From 1 ESG challenge to 5 ML-driven strategic solutions

Capstone 51 slides

Learning Approach

This course demonstrates how machine learning augments human-centered design processes through:

  • Practical Applications: Real-world case studies and hands-on projects
  • Algorithm Understanding: Deep dives into ML algorithms with actual implementations
  • Design Integration: Connecting ML capabilities with design thinking stages
  • Ethical Considerations: Responsible AI and fairness in ML systems

Audio Podcasts

Listen to AI-generated audio summaries of each lecture (created with NotebookLM):

TopicPodcast
ML FoundationsListen
Neural NetworksListen
ClassificationListen
ClusteringListen
NLP & SentimentListen
Generative AIListen
A/B TestingListen
Finance ApplicationsListen
Responsible AIListen

More podcasts coming soon


(c) Joerg Osterrieder 2025